Maroua Mehri
email: maroua.mehri@eniso.u-sousse.tn

Pierre Héroux
email: pierre.heroux@univ-rouen.fr

Rémy Mullot
email: remy.mullot@univ-lr.fr

Jean-Philippe Moreux
email: jean-philippe.moreux@bnf.fr

Bertrand Coüasnon
email: bertrand.couasnon@irisa.fr

Bill Barrett
email: barrett@cs.byu.edu

ICDAR2019 Competition on Historical Book Analysis -HBA2019

Keywords: HBA 1.0 dataset, Historical book analysis, Pixellabeling

In this paper, we present an evaluative study of pixel-labeling methods using the HBA 1.0 dataset for historical book analysis. This study is held in the context of the 2 nd historical book analysis (HBA2019) competition and in conjunction with the 15 th IAPR international conference on document analysis and recognition (ICDAR2019). The HBA2019 competition provides a large experimental corpus and a thorough evaluation protocol to ensure an objective performance benchmarking of pixel-labeling document image methods. Two nested challenges are evaluated in the HBA2019 competition: Challenge 1 and Challenge 2. Challenge 1 evaluates how image analysis methods could discriminate the textual content from the graphical ones at pixel level. Challenge 2 assesses the capabilities of pixellabeling methods to separate the textual content according to different text fonts (e.g. lowercase, uppercase, italic, etc.) at pixel level. During the competition, we received 52 and 38 different teams' registrations for Challenge 1 and Challenge 2, respectively and finally 5 of them submitted their results in each challenge. Qualitative and numerical results of the participating methods in both challenges are reported and discussed in this paper in order to provide a baseline for future evaluation studies in historical document image analysis. The evaluation shows that the method submitted by the NLPR-CASIA team achieves the highest performance in both challenges.

I. INTRODUCTION

Over the last few years, libraries and archives have conducted large digitization programs with cultural heritage documents to guarantee lasting preservation and worldwide access to historical collections. The increasing interest in digital libraries of Google and recently of other leaders and large firms (e.g. Microsoft, IBM, Yahoo and Amazon) proves the major success and effervescence of digital libraries and their rapid growth worldwide, and it poses new specific challenges concerning the preservation and reproduction of historical collections to reinforce its leadership position [START_REF] Stein | Literary research and the American realism and naturalism period: strategies and sources[END_REF]. Providing robust and accurate historical document image analysis (HDIA) methods has been identified as among the major challenges [START_REF] Antonacopoulos | ICDAR 2013 Competition on Historical Book Recognition (HBR 2013)[END_REF]. Indeed, HDIA remains an open issue due to the idiosyncrasies of historical documents, such as the superimposition of information layers (e.g. stamps, handwritten notes, noise, back-to-front interference and page skew) and the variability of their contents and/or layouts, etc. Moreover, HDIA will often be complicated by the unavailability of a priori knowledge about the layout and content, scanning resolution or DI size, etc. On the other side, factors concerning the book edition and digitization properties (e.g. paper quality, printing defects, degradation, black borders, darker areas in the binding margins, flat scan by opening pair of pages, noise generated by the scanner roller and sensor, contrast/brightness level, curvature and compression) further complicates the HDIA task. Therefore, the research community specialized in HDIA is continuing to investigate and provide efficient historical document image segmentation, layout analysis and characterization methods.

Since 2011, many competitions have been proposed for historical document image segmentation and layout analysis in the context the ICDAR and the ICFHR conferences [START_REF] Antonacopoulos | ICDAR 2013 Competition on Historical Book Recognition (HBR 2013)[END_REF]. These competitions are hindered by many issues related to the different provided datasets of historical document images. First of all, the different datasets of historical document images provided in the context of theses contests focus on either a specific kind of document such as historical newspaper layout analysis (HNLA), or a specific application such as handwritten text recognition (HTRtS and RHHT), multi-spectral text extraction (MS-TEx), text line detection (ANDAR-TL), word recognition (ANWRESH), keyword spotting (KWS), classification of medieval handwritings in Latin script. Moreover, the majority of these datasets are composed of pages having similar content and layout characteristics or collected from a single book or collection such as the GERMANA corpus or the RODRIGO corpus [START_REF] Serrano | The RODRIGO database[END_REF]. On the other side, there is a limited number of realistic, comprehensive and flexibly structured datasets of historical document images and their associated ground truths. In addition to these issues which are critical to provide an informative benchmarking of HDIA methods, the lack of a common dataset of historical document images and the appropriate quantitative evaluation measures are also highlighted by researchers.

Therefore, we present in this paper the report of the 2 nd edition of the historical book analysis (HBA2019) competition 1 which is sponsored by the 15 th IAPR international conference on document analysis and recognition (ICDAR2019). The HBA2019 competition provides a comparative study of five pixel-labeling methods using a large experimental corpus of historical document images, the HBA 1.0 dataset [START_REF] Mehri | HBA 1.0: A pixel-based annotated dataset for historical book analysis[END_REF] and an evaluation protocol to address specific issues related to HDIA methods for a collection of several digitized historical books. The HBA2019 competition addresses an important lack in past competitions by providing a means of consistent evaluation and comparison of pixelbased image analysis methods for digitized historical book analysis. The goal of the HBA2019 competition consists of showing that low-level image analysis methods can be tuned on a small training dataset (i.e. a small number of book pages with their associated ground truth provided) in order to deduce automatically the corresponding information on the remaining pages of the analyzed book. One key feature of the proposed experimental corpus for the HBA2019 competition is that it is composed of images that represent all pages of books. Moreover, the HBA2019 competition also evaluates the generalization and adaptation capabilities of low-level image analysis methods as they are applied on a dataset that presents a large variety of ancient books.

The remainder of this paper is organized as follows. Section II presents an overview of the HBA2019 competition and its two challenges. In Section III, a brief description of the HBA 1.0 dataset is introduced. Section IV details the evaluation protocol by outlining the ground truth, the experimental protocol and the accuracy metrics used to evaluate the performance of each participating method in the HBA2019 competition. Each participating method in both challenges of the HBA2019 competition is summarized in Section V. Qualitative and numerical results of the evaluation of the participating methods are reported and discussed in Section VI. Finally, our conclusions and future work are presented in Section VII.

II. HBA2019 COMPETITION

The HBA2019 competition is focused on evaluating the discriminating power of pixel-labeling methods for segmenting graphical contents from textual ones on the one hand, and for separating various text fonts and scales on the other hand, in images representing the pages of historical books. Therefore, two nested challenges are proposed in the HBA2019 competition. The first challenge (Challenge 1) is interested in raising issues related only to how image analysis methods perform for discriminating the textual content from the graphical ones. In the Challenge 1, a binary classification task is evaluated. On the other side, the second challenge (Challenge 2) evaluates the capabilities of pixel-labeling methods to firstly distinguish between text and graphic, and secondly to separate the textual content according to different text fonts (e.g. lowercase, uppercase and italic). In the Challenge 2, a multi-class classification task is assessed. Indeed, the textual content can contain formatting such as many different typefaces and sizes which are associated to the structure level of the analyzed document. The logical level aims to interpret and recognize the different parts that compose a document image and specify the logical relationship between them (e.g. body text, legend, annotation and chapter title).

III. HBA 1.0 DATASET

To provide an informative benchmarking of HDIA methods, many researchers have addressed the need of a realistic (i.e. it must be composed of real digitized document images), comprehensive (i.e. it must be well characterized and detailed for ensuring in-depth evaluation) and flexibly structured (i.e. to facilitate a selection of sub-sets with specific conditions) dataset. Nevertheless, representative datasets of historical document images with their associated ground truths are currently hardly publicly accessible for HDIA. This is mainly due to the intellectual and industrial property rights on the one hand, and the definition of an objective and complete ground truth of a representative dataset of historical document images on the other hand [START_REF] Antonacopoulos | ICDAR 2013 Competition on Historical Book Recognition (HBR 2013)[END_REF]. Thus, in this study we propose to use the dataset associated with the HBA2019 competition 1 , which is the HBA 1.0 dataset. Figure 1 illustrates few book page samples of the HBA 1.0 dataset. More details can be found in [START_REF] Mehri | HBA 1.0: A pixel-based annotated dataset for historical book analysis[END_REF]. The HBA 1.0 dataset which was introduced in [START_REF] Mehri | HBA 1.0: A pixel-based annotated dataset for historical book analysis[END_REF], is composed of 4,436 real scanned ground truthed one-page historical document images from 11 books (5 manuscripts and 6 printed books) in different languages and scripts published between the 13 th and 19 th centuries. The images composing the HBA 1.0 dataset have been collected from the French digital library Gallica 2 . The HBA 1.0 dataset remains exclusive property of the BnF. It is hosted on a server maintained by the HBA2019 competition organizers and will be made publicly available (along with the ground truth) for scientific research use. It will be among the first of publicly released datasets for layout analysis containing annotations at pixel level.

IV. EVALUATION PROTOCOL

In this section, we briefly present the evaluation protocol used to assess the performances of the participating methods in both challenges of the HBA2019 competition by outlining the defined ground truth, the experimental protocol and the used accuracy metrics. More details can be found in the HBA2019 competition Website 1 .

A. Ground truth

A set of classes has been used for each book of the HBA 1.0 dataset to define its ground truth. The ground truth of the HBA 1.0 dataset is obtained by annotating each foreground pixel. The ground truth of each selected foreground pixel has been defined by means of a label indicating the content type/class of the analyzed historical document image. Different labels for the selected foreground pixels with different fonts have been also assigned for evaluating pixel-labeling methods to separate various text fonts. Figure 2 illustrates few examples of ground truthed book pages of the HBA 1.0 dataset. Each selected foreground pixel is marked by a color that symbolizes the corresponding content type. The ground truth of the HBA 1.0 dataset contains more than 7, 58 billion annotated pixels.

B. Experimental protocol

The HBA 1.0 dataset is divided into two sub-datasets, the sample and evaluation datasets. The sample dataset contains two books while the evaluation one is composed of the nine remaining books. Each dataset is composed of a set of training images and a set of test images. The training dataset contains a reduced number of book pages, along with their ground truth. The training images are representative of different contents and layouts of the book pages. On the other side, the test dataset is composed of images representing the remaining book pages. The sample dataset is provided in order to fine-tune the participating methods in the HBA2019 competition (i.e. training or testing).

All pages of the two selected books composing the sample dataset, along with their ground truths are provided. A few pages are selected from each book of the sample dataset to constitute the training dataset. These selected pages should contain all content classes of the analyzed books. The two selected books that form the sample dataset are: Book 04 as a manuscript book and Book 06 as a printed one. The nine selected books that form the evaluation dataset are: Book 01, Book 02, Book 03 and Book 05 as manuscript books, and Book 07, Book 08, Book 09, Book 10 and Book 11 as printed ones. Only the ground truths of the training images of the nine selected books of the evaluation dataset are provided.

The HBA2019 competition aims at evaluating methods, which would automatically annotate an important number of book pages, based on a limited number of manually annotated pages of the same book. The provided limited number of manually annotated pages constitutes the training image dataset. Therefore, it is ensured that each class of content type is represented in the set of the training pages for each book. It is worth pointing out that the content classes in the HBA 1.0 dataset vary from one book to another book and have very different headcounts. Indeed, the textual content is predominant in monographs, compared with the graphical content. Moreover, among the textual content a great majority represent the body text while other character fonts are more marginal. This imbalanced headcounts between classes varies from one book to another book in the HBA 1.0 dataset. There is surely a great deal of imbalanced headcounts between classes in the same book. The class headcounts in the training dataset are not thereby similar to the test dataset. Indeed, the minority classes are adequately represented in the training dataset in order to ensure an appropriate learning task. However, unlike the minority classes, the majority classes are clearly less represented in the training dataset in comparison with the class headcounts in the test dataset. These requirements have been satisfied in the selection of the training pages in the sample dataset. Similarly, these rules are also applied to the selection of the training pages in the evaluation dataset. It is worth noting that the prime difficulty of the experimental protocol of the HBA2019 competition lies in the experimental corpus which contains different types of content in historical books published at different eras such as printed books from the 19 th century or manuscripts from the 13 th century.

C. Evaluation metrics

To evaluate the performance of the participating methods in the HBA2019 competition, the confusion matrix is computed based on the ground truth and the obtained results. From the confusion matrix, several per-pixel classification accuracy metrics are computed [START_REF] Liu | Web data mining: exploring hyperlinks, contents, and usage data[END_REF].

• The classification accuracy rate (CA) metric corresponds to the ratio of the true classified predicted pixels and the total number of pixels. • The F-measure (F) corresponds to a score resulting from the combination of the precision and recall accuracies by using a harmonic mean. It assesses both the homogeneity and completeness criteria of a clustering result.

• The weighted F-measure (WF) corresponds to a score resulting from the computation of F-measure for each label, and finding their average weighted by the number of true instances for each label. It takes the label imbalance into account. These classical per-pixel classification accuracy metrics are calculated for each content type independently for each book. This shows that whether a pixel-labeling method behaves uniformly among all the books or if, conversely, it achieves a different level of performance for different books. On the other hand, computing these classification accuracy metrics for each content type helps to identify which methods have high performance for specific content types (or for one particular book), even if their overall performance is not so high. It is worth noting that computing the overall classification accuracy metrics is not a good way for evaluating the performance of an image analysis method, as it would mainly measure the performance on the majority classes due to the imbalanced headcounts between classes. The performance measures are computed for both challenges at pixel level. Both the performance measures for each book of the HBA 1.0 dataset and the overall performance are calculated. The evaluation tools used to evaluate the two challenges of this study are available to download 3 .

V. PARTICIPATING METHODS

In this section, brief descriptions of the five participating methods for both challenges are reported.

A. Barney Smith's method (M1)

Barney Smith's method (M1) which is proposed by Elisa H. Barney Smith from Boise State University, focuses on defining heuristics deduced from analyzing connected component, word and line characteristics.

B. Stewart and Barrett' method (M2)

Stewart and Barrett' method (M2) is proposed by Seth Stewart and Bill Barrett from Brigham Young University. It uses a fully convolutional network (FCN) architecture, which is based substantially on the work found in [START_REF] Tensmeyer | Document image binarization with fully convolutional neural networks[END_REF]. Training occurs by applying stochastic gradient descent on randomly sampled image crops with a per-class quota. Stewart and Barrett further solve the problem of class imbalance by periodically computing per-class F-measures, which are used to update the balance of each class in the minibatches, prioritizing the more difficult classes.

C. Li et al.' method (M3)

Li et al.' method (M3) which is proposed by Xiao-Hui Li, Fei Yin and Cheng-Lin Liu from the National Laboratory of Pattern Recognition (NLPR) -Institute of Automation of Chinese Academy of Sciences (CASIA) -University of Chinese Academy of Sciences, is based on 3 http://hba.litislab.eu/evaluation scripts/hba evaluation scripts.zip a FCN model. To improve the performance of the FCN model, Li et al. apply the following modifications. First, to solve the problem of scale inconformity they resize the original images and ground truth maps to the same short edge of 2048 pixels. Second, for data enhancement they apply random scaling and rotation, then they randomly cut the original images and ground truth maps to small patches with size of 512 ×512 pixels. Third, they use weighted cross entropy as our loss function in which the weights are set as inverse class frequency in each batch. For pixels which are labeled as background in the ground truth maps but predicted as foreground by the FCN, they set their labels to background. For pixels which are labeled as foreground in the ground truth maps but predicted as background by the FCN, they use the labels of their nearest foreground neighbor as their predicted label.

D. Grüning et al.' method (M4)

Grüning et al.' method (M4), called Argus-PixelLabeler is proposed by Tobias Grüning, Tobias Strauß and Gundram Leifert from CITlab (University Rostock) -Planet AI GmbH. The Argus-PixelLabeler is based on an ARU-Net network introduced in [START_REF] Grüning | A two-stage method for text line detection in historical documents[END_REF]. The ARU-Net is trained to distinguish between the 6 different classes of interest. The original images are down-scaled such that they have a width of roughly 1600 pixels. Furthermore, the ground truth pixel information is dilated to increase its ratio to the entire set of pixels and to reduce errors caused by Otsu's binarization which are present in the competition data. We trained the neural network with randomly scaled versions of the training data. A simple post-processing is performed. It assigns the class of the closest labeled foreground pixel to foreground pixels, which are not labeled by the ARU-Net. No additional post-processing steps are performed.

E. Zharkov's method (M5)

Zharkov's method (M5) which is proposed by Andrey Zharkov from ABBYY-LLC, is based on a convolutional neural network (CNN) architecture. Zharkov trains the used CNN for semantic segmentation on entire evaluation training set and sample training set. After that he fine-tunee the weights by training with lower learning rate on each particular book for few epochs. The network architecture has 3 initial convolutions to reduce spatial image dimension by 4 then 6 more dilated convolutions with exponential increase in dilation to capture some context, then last convolution for dense predictions of class probabilities. The network is trained using mean cross-entropy loss on foreground pixels with random crops and small noise/color augmentations.

VI. RESULTS

A brief overview of the obtained qualitative and numerical results of the five participating methods: Barney Smith's method (M1), Stewart and Barrett' method (M2), Li et al.' method (M3), Grüning et al.' method (M4) and Zharkov's method (M5), is presented in this section. More detailed results will be posted on the HBA Web site 1 .

A. Qualitative results

Many examples of the results of the five participating methods on the HBA 1.0 dataset for the Challenge 2 are shown in Figure 3. First, we note that the M1 and M2 methods behave differently among all the books of the HBA 1.0 dataset. For instance, we observe that the M1 method has more difficulty separating different textual fonts (cf. Figure 3(a)) compared with the M2 method (cf. Figure 3(f)). We also see that the textual regions (green) are more homogeneous when using the M1 method on Book 08 (cf. Figure 3(c)) and Book 09 (cf. Figure 3(d)) compared with the M2 method (cf. Figures 3(h) and 3(i)). Nevertheless, we observe that the graphical regions (blue) are more homogeneous when using the M2 on Book 08 (cf. Figure 3(h)) and Book 09 (cf. Figure 3(i)) compared with the M1 method (cf. Figures 3(c

Barney Smith's method (M1)

B. Quantitative results

It is necessary to assess quantitatively the results in order to have a conclusion of which method is well suited for firstly segmenting graphical regions from textual ones, and then for discriminating text in a variety of situations of different fonts and scales. The evaluation results of the five participating methods are presented in terms of the classification accuracy rate (CA), the F-measure (F) and the weighted F-measure (WF) on the nine books of the evaluation dataset (Book 01, Book 02, Book 03, Book 05, Book 07, Book 08, Book 09, Book 10 and Book 11). The performance evaluation of the five participating methods in both challenges of the HBA2019 contest are presented in Table I.

First, we note that the computed accuracy values are not sufficiently congruent. Indeed, we note a slight difference in the performance of the F comparing the two other computed accuracy metrics (CA and WF). This is due to the nonconsideration of the imbalanced headcounts between classes in the same book when the F metric was computed. We also observe that the best results of WF values are obtained by using the M3 method on Book 08 (99.97% and 99.98% for the Challenge 1 and Challenge 2, respectively). This can be justified by the particularities of Book 08. Indeed, the majority of the Book 08 pages have double-column layouts and contain single text font and graphics. Moreover, we show that the worst WF performances are obtained by using the M1 method on Book 11 (86.33%) for the Challenge 1 and Book 07 (61.81%) for the Challenge 2. This can be explained by the insufficient number of the training images of Book 07 since it has a limited number of pages and large variability of page layout and content (i.e. six predefined classes in the ground truth). In addition, we note that the M3 method achieves the best performance on Book 01, Book

VII. CONCLUSIONS AND FUTURE PERSPECTIVES

In order to address an important lack in past benchmarking studies and contests, this work evaluates five pixellabeling methods for HDIA. The obtained results confirm that our contest allows a consistent evaluation and a fair comparison of low-level image analysis methods for HDIA on the one hand, and that methods based on deep architectures provide better performance than the ad-hoc methods on the other hand. Moreover, the evaluation shows that the method submitted by Li et al. from the NLPR-CASIA team achieves the highest performance in both challenges.

An important need has been recently highlighted by researchers that consists in providing a representative regionbased annotated dataset of historical document images in order to ensure the evaluation of page content classification methods at region/block level. Thus, we are currently working on developing a novel ground truthing toolkit for layout analysis of historical document images which ensures automatic generation, visualization and editing of regionbased ground truths of document images based on the pixelbased ground truth of the HBA 1.0 dataset.

( a )Figure 1 .

 a1 Figure 1. Sample book pages of the HBA 1.0 dataset.

  (a) Book 01 (b) Book 03 (c) Book 06 (d) Book 07 (e) Book 10

Figure 2 .

 2 Figure 2. Sample ground truthed book pages of the HBA 1.0 dataset.

( a )

 a Book 01 (b) Book 03 (c) Book 08 (d) Book 09 (e) Book 10 Stewart and Barrett' method (M2) (f) Book 01 (g) Book 03 (h) Book 08 (i) Book 09 (j) Book 10 Li et al.' method (M3) (k) Book 01 (l) Book 03 (m) Book 08 (n) Book 09 (o) Book 10 Gr üning et al.' method (M4) (p) Book 01 (q) Book 03 (r) Book 08 (s) Book 09 (t) Book 10 Zharkov's method (M5) (u) Book 01 (v) Book 03 (w) Book 08 (x) Book 09 (y) Book 10

Figure 3 .

 3 Figure 3. Examples of resulting images obtained when using Barney Smith's method (M1), Stewart and Barrett' method (M2), Li et al.' method (M3), Grüning et al.' method (M4) and Zharkov's method (M5) for the Challenge 2.

  ) and 3(d)). By comparing the visual results, we show that the M3 method (cf. Figures 3(k) → 3(o)), the M4 method (cf. Figures 3(p) → 3(t)) and the M5 method (cf. Figures 3(u) → 3(y)) give the best results in terms of the homogeneity of the textual and graphical region content.

  02, Book 03, Book 05 and Book 07 for the Challenge 1, and on Book 02, Book 03, Book 05, Book 07 and Book 10 for the Challenge 2. On the other side, the M4 method achieves the best performance on Book 08, Book 09 and Book 11 for the Challenge 1, and Book 01, Book 08 and Book 09 for the Challenge 2. Besides, the M5 method

Table I EVALUATION

 I RESULTS OF THE PARTICIPATING METHODS. M4 and M5 denote the Barney Smith's method, Stewart and Barrett' method, Li et al.' method, Grüning et al.' method and Zharkov's method. CA, F and WF denote the classification accuracy rate, F-measure and weighted F-measure, respectively. The higher the values of the computed metrics, the better the results. The values which are quoted in red and green are considered as the lowest and highest values, respectively for each participating method. Table cells whose background are yellow note the best performance for each book with respect to the WF metric.achieves the best performance on Book 10 and Book 11 for the Challenge 1 and Challenge 2, respectively. Finally, we conclude that that the methods based on deep architectures provides better performance than the ad-hoc methods (based on different image processing techniques, such as connected component) especially when there is an insufficient number of the training images and large variability of page layout and content. Moreover, we see that the method submitted by Li et al. from the NLPR-CASIA team achieves the highest WF performance in both challenges (99.80% and 99.08% for the Challenge 1 and Challenge 2, respectively) followed by Grüning et al. from the CITlab team (99.75% and 99.06% for the Challenge 1 and Challenge 2, respectively), and Zharkov from the ABBYY-LLC team (99.74% and 98.93% for the Challenge 1 and Challenge 2, respectively).

			CA	Challenge 1 F	WF	CA	Challenge 2 F	WF
	Book 01	M1 M2 M3 M4 M5	86.15 97.35 99.96 99.95 99.95	92.49 96.61 99.72 99.68 99.71	92.25 98.45 99.96 99.95 99.95	82.02 97.20 99.76 99.87 99.72	75.04 94.74 98.37 99.21 98.00	87.82 98.30 99.76 99.87 99.72
	Book 02	M1 M2 M3 M4 M5	94.00 92.93 99.50 99.46 99.37	75.81 77.63 96.38 95.97 95.09	94.27 93.26 99.51 99.47 99.38	93.97 92.93 99.49 99.45 99.35	75.81 77.65 83.65 82.97 82.42	94.24 93.26 99.50 99.46 99.36
	Book 03	M1 M2 M3 M4 M5	98.26 94.92 99.78 99.69 99.69	91.53 83.46 98.63 98.12 98.04	98.46 95.17 99.78 99.69 99.69	98.26 94.92 99.73 99.69 99.69	91.53 83.46 98.62 98.12 98.04	98.46 95.17 99.73 99.69 99.69
	Book 05	M1 M2 M3 M4 M5	94.98 96.81 99.90 99.89 99.77	89.06 96.89 99.76 99.73 99.44	95.26 98.29 99.90 99.89 99.77	89.54 96.61 99.84 99.80 99.61	89.26 91.09 97.14 99.47 99.27	89.81 98.09 99.84 99.80 99.61
	Book 07	M1 M2 M3 M4 M5	93.50 98.83 99.71 99.49 99.53	80.26 98.39 98.85 97.95 98.10	93.67 99.24 99.71 99.49 99.53	61.70 93.21 94.11 93.94 93.28	43.84 92.81 93.54 93.01 92.04	61.82 93.60 94.11 93.94 93.28
	Book 08	M1 M2 M3 M4 M5	99.01 95.17 99.96 99.97 99.95	98.46 92.39 99.93 99.97 99.93	99.17 95.41 99.96 99.98 99.96	99.01 95.17 99.96 99.97 99.95	98.46 92.39 99.93 99.97 99.93	99.17 95.41 99.96 99.98 99.96
	Book 09	M1 M2 M3 M4 M5	98.69 92.27 99.89 99.90 99.88	97.61 86.97 99.74 99.77 99.74	98.92 92.38 99.89 99.91 99.89	98.67 92.26 99.86 99.87 99.87	97.63 86.99 85.11 82.10 87.71	98.90 92.38 99.86 99.87 99.87
	Book 10	M1 M2 M3 M4 M5	78.67 95.12 99.65 99.47 99.66	88.17 97.38 99.60 99.41 99.62	87.33 97.27 99.65 99.47 99.66	77.66 94.78 99.35 99.29 99.14	73.05 90.16 86.14 86.10 81.85	86.21 96.92 99.35 99.29 99.14
	Book 11	M1 M2 M3 M4 M5	76.22 87.29 99.82 99.89 99.84	81.47 47.39 93.62 95.90 94.04	86.33 92.53 99.82 99.90 99.84	75.77 86.80 99.60 99.61 99.74	77.67 41.83 78.89 96.37 83.82	85.82 92.01 99.60 99.61 99.75
	Overall	M1 M2 M3 M4 M5	91.05 94.52 99.80 99.75 99.74	88.32 86.35 98.47 98.50 98.19	93.96 95.78 99.80 99.75 99.74	86.29 93.77 99.08 99.05 98.93	80.25 83.46 91.27 93.03 91.45	89.14 95.01 99.08 99.06 98.93
	M1, M2, M3,

http://hba.litislab.eu/

http://gallica.bnf.fr

ACKNOWLEDGMENT

The authors would like to acknowledge the French national library (BnF) for providing access to the French digital library Gallica.


Jacques Pothier

Imagery and the Making of The Hamlet: Of Snopeses and Cows

In 1962 at West Point Faulkner makes a rare comment on his process of writing. He is answering the question of a student about how he "goes about writing": "The stories with me begin with an anecdote or a sentence or an expression, and I'll start from there and sometimes I write the thing backwards -I myself don't know where any story is going. I write -I'm dealing simply with people who suddenly have got up and have gotten into motionmen and women who are moving, who are involved in the universal dilemmas of the human heart." (Faulkner at West Point 80-81) After giving away that a story stems from a textual fragment, from language, Faulkner cuts himself off, corrects what he was starting to say and falls back on the bland familiar humanistic rhetoric of the late career. 1 He goes on to say: "Then when I have a lot of it down, the policeman has got to come in and say, 'Now look here, you've got to give this some sort of unity and coherence and emphasis,' the old grammatical rulesand then the hard work begins." The old grammatical rules: the work on the text is described in terms of the basic organization of language: grammar, style, rules.

But what generates the work is what disrupts codes and overturns boundaries.

In this paper I can only outline how this process can be seen to be at work in the development of the Snopes material, down to its major achievement, The Hamlet. 2 1 In a related earlier occurrence, Faulkner is reported as telling the students of the University of Virginia that the idea of a novel "begins with the thought, the image of a character, or with an anecdote . . .

The plot of The Hamlet is largely made up of the short stories that Faulkner had told in the preceding decade, illustrating the constant feud between the Snopeses and a country community. The realistic rendering of everyday life in the rural backcountry is vivid, even movingly realistic. It has been common enough to see The Hamlet as a loosely connected chronicle of a backcountry village, with a lot of local color thrown in. It is that. But it is also a highly rhetorical creation, and the engine of its development, as I shall argue here, is largely the expansion of matricial fragments of language -"expressions," to use Faulkner's very words. The whole book sticks together because of the florid imagery with which every page is pervaded. The web of imagery that holds the sociological mimesis together therefore stylizes the realistic depiction.

Joseph Blotner notes that "The Hill" reveals "the central fact about [Faulkner's] style as a fiction writer": "he thought and wrote in poetic terms within a realistic framework which provided sufficient room for symbolist techniques." (Blotner 332) Lothar Hönnighausen concludes his book on Faulkner's early work saying that "the most interesting texts of the mid-twenties are . . . those like "Nympholepsy" where the idealized forms from The Marionettes are used to structure "Realistic" material . . . the tensions between stylization and mimesis develop into the chief organizing force of Faulkner's mature prose." (Hönnighausen 180) It is this development that I am interested in here, with a few examples of how Faulkner's imagery developed and multiplied between the early drafts of Father Abraham and the completed typescript of The Hamlet.

The titles of Faulkner's drafts and early short story versions of the Snopes project are often cryptic, emphasizing reference to earlier, generically determined material rather than rural subject matter: biblical stories in Father Abraham and "Abraham's Children," Greek tragedy in "As I Lay Dying," choral music in "Aria con Amore," symbolist poetry in "Afternoon of a Denis Diderot, 1984) and was continued for the Pléiade edition of the French translation of The Hamlet, with Michel Gresset and Didier Coupaye.

Cow," Persian poetry in "Omar's Eighteenth Quatrain," otherwise renamed "Lizard's in Jamshyd's Courtyard," a title which only pretends to be less esoteric. Fewer titles are mimetic, like "Spotted Horses," "Fool About a Horse," "The Hound" and eventually "Barn Burning". 3 "The Peasants," the tentative title to the novel The Hamlet, is only apparently referential, as it also refers to a generic literary model, Balzac's novel with the same title.

The manuscript and typescript versions of Father Abraham are both interrupted at different points, but in both cases Faulkner's writing seems to stumble on a chunk of imagery too big for the Snopes reality to swallow. The manuscript ends with a last page full of false starts and cancelled passages which sets Suratt's buggy on the stage of Marionettes:

But supper over, you emerged into a different world. A world of lilac peace, in which Varner's store and the blacksmith shop were like sunken derelicts in the motionless and forgotten caverns of the sea. No sound, no movement; no tide to knock their sleeping bones together. And yet it was not quite night. The west was green and tall and without depth, like a pane of glass; through it a substance that was not light seeped in sourceless diffusion, like the sound of an organ. Suratt's buckboard stood at the hitching rail. 4 In the last lines of the interrupted typescript, the text tries to compare Mrs Armstid's dogged shyness to Agamemnon as he lay dying, slain by Clytemnestra:

. . . and they stood excruciatingly and watched Mrs. Littlejohn, and presently Henry's wife entered with her desolate dog's eyes and stood at the foot of the bed with her (118-183; 424-434; henceforth, US in the text); earlier drafts of "As I Lay Dying," "The Peasants," "Aria Con Amore" (like Father Abraham, early versions of "Spotted Horses"), "Omar's Eighteenth Quatrain" ("Lizards in Jamshyd's Coourtyard"), "Fool About a Horse," "Afternoon of a Cow," "Barn Burning" are in volume 15.1 of the William Faulkner Manuscripts. 4 Father Abraham 71. See WFM 2: 25 for the discarded material.

worn quiet hands clasped across her middle and her face in shadow. 5 The failure to complete the Father Abraham text suggests that Faulkner endows his Snopes material with life when he proceeds from imagery to plot, not when he tries to force imagery into plot.

The tentative titles to the Snopes story as well as the famous two opening paragraphs of Father Abraham witness to Faulkner's attempt to account for the irreducible otherness of the Snopeses through a wealth of distorting imagery: turning away from the realistic agenda that the rise of the unpoetic Snopeses would seem to suggest, Faulkner compares his characters to inanimate objects, animals or literary or historical types. In Father Abraham the narrative starts with two paragraphs of cultural comparisons between Flem, the archetypal Snopes, and various figures from mythology or the Bible; conversely, in the completed novel, the story is introduced by an apparently strictly historical introduction to the world of Frenchman's Bend.

A study of the genetic material reveals that Faulkner lets this apparently realistic description drift and expand out of the original material along lines which are inspired by metonymy and lexical puns. In the Rowan Oak Papers at the University of Mississippi, there is a one-page manuscript portrait of Flem Snopes behind the plate-glass window of his bank which could well be a draft of the famous opening section of Father Abraham: Much of the material in The Hamlet is in germ here: Flem's unblinking gaze, his face the color of uncooked dough and his chewing:

It
One moment the road had been empty, the next moment the man stood beside it, at the edge of a small copsethe same cloth cap, the same rhythmically chewing jaw materialised apparently out of nothing . . . His face was as blank as a pan of uncooked dough.7 * Let's start with Flem's mouth: at first tobacco-chewing would be a tell-tale feature connotating his poor white origin -Faulkner was getting ready to write a novel of warning against the rise of the redneck, as Balzac had done with The Peasantry. The second paragraph of the seminal Rowan Oak papers fragment outlined the changes in the sociology of a little town in the twentiesthe changes in the new South which Phil Stone and his younger friend were so worried about. Flem is the redneck come to town and taking over economy and politics. He embodies the nightmare of the Southern gentry and the desecration of the Southern dream. In Faulkner's "The Hill" or "Nympholepsy" also tobacco signifies the decline of the old South: "he could not see that the sonorous simplicity of the court house columns was discolored and stained with casual tobacco" ("The Hill," Early Prose and Poetry 91); "you could not see that pale Ionic columns were stained with casual tobacco."

("Nympholepsy," US 332) But as Faulkner rewrites his portrait of Flem from draft to draft Flem's tobacco chewing, the attribute of the hated redneck, a metonymy, becomes metaphoric: Flem becomes his mouth.

If we shift our attention to Father Abraham, we can see that in the two-paragraph opening Faulkner mentions Flem's mouth three times, as if insistence could reinforce meaning: "Thus he comes into the picture, thus he goes out of it, ruminant and unwinking and timeless."the cowlike feature of the redneck is one of three adjectives. Faulkner goes on (note the neutral possessive): " its mouth like one of those patent tobacco pouches that you open and close by ripping a metal ring along the seam" makes him look like the tobacco he chews as the redneck he will always be. A marginal addition in the holograph manuscript drives the nail home: "He chews tobacco constantly and steadily and slowly with a slow thrusting motion", which is toned down to "He chews tobacco constantly with a slow thrusting motion" in the typescript (WFM 2: 1, 27-8, 81-2). Incidentally, except for the reference to tobacco, this is the expression by which Faulkner mentions the first apparition of Flem in The Hamlet:

"[Jody] saw suddenly in one of the sashless windows and without knowing when it had come there, a face beneath a gray cloth cap, the lower jaw moving steadily and rhythmically with a curious sidewise thrust" (H 21).

The rest of the ten-page initial section of Father Abraham recurrently refers to Flem's chewing habit, using it to characterize his social background: when he married his wife in Frenchman's Bend "his tobacco pouch seam of a mouth was always stained at the corner with snuff. . . . The only concession he had made to urban life was to substitute chewing tobacco for his snuff." (WFM 2: 34,88,144) This brilliant dialogue is typical of the use of style and imagery in The Hamlet. Faulkner is using the image -I chew up a nickel as a metonymy for a nickel's worthas the literal tenor at the same time as it is a vehicle. In his pocket book of a mouth, Flem Snopes would naturally be chewing nickelsuntil the suption was out of them, when, obviously, he would have to find more nickels to chew.8

In his early references to the Snopes clan, Faulkner often refers to them as rodents, which is at once a moral and a visual metaphoror better said, the Snopeses physically embody their social position as parasites and pests by visually chewing constantly. Beulah, the cow in the "Afternoon of a Cow" sketch, named after a biblical symbol of plenty, is present through several avatars. We first see the Snopeses' closeness to cattle in the marginal presentation of Flem's sisters in chapter 1: they are compared to "a pair of heifers" (H 15). Ratliff later says that "Flem has grazed up the store and he has grazed up the blacksmith shop …" (H 77) The cow motif then pervades the central two books of The Hamlet: in Book 2 Beulah the cow becomes Eula, the potato-chewing female child, secretly courted by Labove, whose name is destiny; in Book 3 the ruminant that a cousin of the archetypal chewing Snopes falls in love with confirms that birds of a feather flock together, as the fellow saysas long as the reader accepts to merge literal meanings and the dreamlike associations which the text suggests.

To the scholar, the manuscripts of The Hamlet are uncommonly difficult to decipher, but there is in general little rewriting, if we leave out the obvious variants in passages that were understandably revised, if only for style, to be published as separate short stories. 9 The manuscript of the whole last third of the novel is missing, and possibly never existed. But one passage proves dauntingsection 2 of Book 3, chapter onethe Ike and cow idyll. As

Faulkner often did to keep track of the time spent on an important manuscript, for instance a book, he noted down the date in the margin of the first page of his drafts: February 10, 1939.

Ike Snopes's love-story is the play within the play, the literally pastoral idyll setting off the failed, parodic fairy-tale love-story between the frog-like Flem and Eula the queen-bee. The meaning of this famous but difficult passage is enhanced by Faulkner's intratextuality and self-parody.

The manuscripts reveal that Faulkner considered a subtitle, "The Morning and the Evening,"

as if planning to try for a separate publication of the passage, as he had done for "Barn Burning". Indeed, Ike's love story actually echoes and continues "Barn Burning" in a shared expansion of the material of "The Hill": in the three texts or fragments, a column of smoke calls the subject's attention to a fire; in "Barn Burning" the narrator becomes fascinated by the white columns of a house which he thinks looks like a courthouse, and this beauty leads him to betray his blood and escape to the woods, where he watches the wheeling constellations and walks into the trees. The last sentences of the short story really echo those of "The Hill": "He went on down the hill, toward the dark woods within which the liquid silver voices of the birds called unceasingthe rapid and urgent beating of the urgent and quiring heart of the late spring night. He did not look back." (CS 25) In " The Long Summer"

Ike passes the crest of a hill to meet his beloved by the river, and then by the spring, a development which was suggested or dreamed of in "Nympholepsy". In the end of the idyllic day they spend together, he merges with the rhythms of nature and the "marching constellations" as Sarty Snopes does in "Barn Burning." One key word toward the end of the Hamlet section anchors it into intratextuality, as Faulkner mentions the "nympholept noon."

(H 205)

The manuscripts also reveal an early division of the passage into three sections, a stylistic feature that suggests a prose poem: the morning, the afternoon, the evening. The afternoon points to the earlier "Afternoon of a Cow," and the other two titles can be seen as a mere metatextual expansion of this title, before and after, since they do not refer to anything which is set at a time of the day. "The morning" begins with an analeptic narrative of Ike's first attempts at courting the cow. It goes on to describe the rescue of the princess from the dragon, i.e. the day when Ike saw the smoke and rescued his cow from the grass-fire. In "the afternoon" Houston finds them and takes the cow back to her barn and they escape again;

"the evening" is a lyrical description of a full day's picnic that he can now have with his lover, starting before dawn in the barn, enjoying the whole course of the day in a fusion with the forces of nature that she represents. Faulkner goes about writing by expansion and division: the scene of the discovery by Houston is doubled, as an inserted analepsis recounts how Houston first discovered the lovers, three days before the fire. Thus the brief dialogue between Houston, Ike Snopes and the hound comes to be divided by ten pages in The Hamlet, a process which may be compared with the reading of Charles Bon's letter to Judith in Absalom, Absalom!

In the second paragraph of the section on Eula's childhood as well as in the section on the cow, Faulkner uses an uncommon word: integer. Thus he subtly points out a parallel between 1/3/1998 5:16:00 PM Eula and the cow, as a poet might anchor a metaphor by repeating a key word in two stanzas.

The child Eula "seemed to be not an integer of her contemporary scene, but rather to exist in a teeming vacuum in which her days followed one another as though behind sound-proof glass," (H 105-6), while as he sweeps Ike Snopes can still see his cow, "integer of spring's concentrated climax, by it crowned, garlanded." (H 186) Coming as it does a the end of a paragraph, the word garland directs the reader to the fourth stanza of Keats's "Ode on a Grecian Urn":

Who are these coming to the sacrifice?

To what green altar, O mysterious priest, Lead'st thou that heifer loing at the skies, And all her silken flanks with garlands drest?

In turn, this association sends the attentive reader back to the concluding sentence of the Eula paragraph where the word integer occurred, in which another procession is shown, also reminiscent of ancient times: "Mrs Varner in her Sunday dress and shawl, followed by the negro man staggering slightly beneath his long, dangling, already indisputable female burden like a bizarre and chaperoned Sabine rape." (H 106)

The possibility of a cross reference back to Eula is not provided in the first draft of the section on Ike's love story, which in its shorter initial form is closer to "Afternoon of a Cow": it centers on the fire which Ike Snopes sees from the Littlejohn boarding-house. Both texts have to do with a grass-fire from which cattle has to be rescued, and in both stories a Pegasus-like horse unexpectedly materializes out of the flames to frighten the cow away. This is an instance of an earlier simile materializing into story-matter: "Carcassonne" focussed on the vision of "a buckskin pony with eyes like blue electricity and a mane like tangled fire" while in the longer "As I Lay Dying" draft of "Spotted Horses" Suratt told how he had been attacked by a pony "soarin over that boy's head like a hawk, with its eyes lookin like two of 1/3/1998 5:16:00 PM these hyer colored electric lights and its mane like a grass fire." (CS 895,899;WFM 15.1: 36) The simile appeared also in the Hamlet version of "Fool About a Horse": "its mane and tail swirling like a grass fire." (H 37) "Afternoon of a Cow" and The Hamlet provide a striking example of the way Faulkner cuts and pastes, keeping but re-arranging the words and expressions of one text as if they were so many blocks to build up other meanings. "Afternoon of a Cow" was a parodic fable like the "Ballade d'une vache perdue" which had been published in The Mississippian as a pastiche of The smoke lay like a wall before him; beyond it he could hear the steady terrified bellowing of the cow. He ran into the smoke and toward the voice. The earth was now hot to his feet. He began to snatch them quickly up . . . he heard the hooves and as he paused, his breath indrawn, the horse appeared, materialized furiously out of the 1/3/1998 5:16:00 PM smoke, monstrous and distorted, wild-eyed and with tossing mane, bearing down upon him. (H 190) The horse is not named in The Hamlet, but the abstract wall in his name occurs as the concrete wall of smoke. It may therefore be legitimate to look back at an intriguing detail in the first page of the novel: although Frenchman's Bend has been described in close to half a dozen texts before, only in The Hamlet do we learn that the Frenchman's estate was originally a grant. However the very sound of "grant" had been associated with it ever since Father Abraham, in which it was first noted that "Grant swept through the land on his way to Chickamauga." (Father Abraham 15) "The Hill"and "Nympholepsy" introduce the country scenery and the poor white characters of the Snopes saga in Faulkner's writing: there is the blacksmith's shop, the court-house columns which foreshadow Labove and his fantasies are "a dream dreamed by Thucydides" (US 331). In The Hamlet the tieless casual is an idiot, unaware of the day's labor behind him and ahead of himexcept as it divides him from his lover: "Because he cannot make one with her through the day's morning and noon and evening" (H 183) when he cannot find intimacy, just as the protagonist of "Nympholepsy" resents the lack of privacy in the workday. Ike Snopes an the casual both go to meet their strange lovers in a bottom where a creek runs. If we think of these two earlier texts, we understand that it is given to Ike Snopes, who in Ratliff's perception "had been given the wordless passions but not the specious words" (H 217), to make true the merging with the natural elements which the casuals in the earlier stories had all but intuited and eventually missed. a tiny viciously depthless cryptically balanced splash like an enigmatic punctuation symbol against the expanse of white shirt which gave him Jody Varner's look of ceremonial heterodoxy raised to its tenth power and which postulated to those who had been present on that day that quality of outrageous overstatement of physical displacement which the sound of his father's stiff foot made on the gallery of the store that afternoon in the spring. . . . it was told of him later, after he had been made president of his Jefferson bank, that he had them made for him by the gross. (H 64)

Here it is the rumor that turns the metaphor into story: as Flem's personal ambition is to merge with the popular image of the successful businessman, it is only natural that he should turn the most eccentric and personal piece of garment into a signifier of mass depersonification. In The Town Gavin Stevens will likewise note that the new furniture he ordered for his house makes him think of a legend in a wholesale furniture catalogue: "This is ----. The Town. New York : Random House, 1957.

1/3/1998 5:16:00 PM

Faulkner's symbolist

  poem "Ballade des femmes perdues". But The Hamlet, full of rodents, horses and cows, reminds one of the world of the Uncle Remus tales by Joel Chandler Harris. In the two following passages, first from "Afternoon of a Cow," second from The Hamlet, the Snopes is face to face in the smoke with the stygian horse which might have been the one that came out of the burning barn in the novel As I Lay Dying. The floor suggests the Hell that Flem visits at the end of Book 2 of The Hamlet: [The smoke] covered the entire visible scene, although ahead of us we could see the creeping line of conflagration beyond which the three unfortunate beasts now huddled or rushed in terror of their lives. Or so we thought until, still led by Mr. Faulkner and hastening now across a stygian and desolate floor which almost at once became quite unpleasant to the soles of the feet and promised to become more so, something monstrous and wild of shape rushed out of the smoke. It was the larger horse, Stonewall . . . (US 426)

  neither a Copy nor a Reproduction. It is our own Model scaled to your individualRequirements." (T 221) The Snopeses do not talk: but the objects they touch or use produce "overstatement"an overflow of meaning through figurative language which comments on the inadequacy of mouthsounds in the world of mass consumption that they represent. Theirs is the modern world of road signs and billboards. We saw that Flem's chewing mouth pervades The Hamlet, but in another avatar he partakes of the raw energy of the flaming 1/3/1998 5:16:00 PM horses of "Carcassonne"which are both billboards and animals, as Ratliff suggests in this passage from the first paragraph of the short story "Spotted Horses": ". . . Here I was this morning pretty near halfway to town, with the team ambling along and me setting in the buckboard about half asleep, when all of a sudden something come swurging up outen the bushes and jumped the road clean, without touching hoof to it. It flew right over my team, big as a billboard and flying through the air like a hawk." (US 165)The following physical portrait of Flem in chapter 3 of The Hamlet as a bunch of uncoordinated signifiers will serve as an open conclusion to this essay: a thick squat soft man of no establishable age between twenty and thirty, with a broad still face containing a tight seam of a mouth stained slightly at the corners with tobacco, and eyes the color of stagnant water, and projecting from among the other features in startling and sudden paradox, a tiny predatory nose like the beak of a small hawk. It was as though the original nose had been left off by the original designer or craftsman and the unfinished job taken over by some viciously maniacal humorist or perhaps by one who had only time to clap into the center of the face a frantic and desperate warning. (H 57) I would like to finish on this implicit portrait of the cubist artist as a "viciously maniacal humorist."Works citedBlotner, Joseph. Faulkner : A Biography.Two-volume ed., New York : Random House, 1974. Creighton, Joanne. William Faukner's Craft of Revision: The Snopes Trilogy, The Unvanquished and Go Down, Moses. Detroit: Wayne State University, 1977. Faulkner, William. The Hamlet, 1940. New York: Vintage International, 1991.

3

  Father Abraham was published as a book by James B. Meriwether; volume 2 of the William Faulkner Manuscripts series (henceforth, WFM in the text) makes available the manuscripts of Father Abraham and "Abraham's Children." Except for "Barn Burning", which was the first short story in Faulkner's Collected Stories volume (henceforth, CS in the text), the magazine versions of the published stories are available in Blotner's Uncollected Stories of William Faulkner

can begin here, with Flem himself sitting in a new Mission oak chair, behind

  If there is either lust or alarm in his gaze, it does not show as, a little obese now from sedentation, a little flabby, his flesh harmless and in color and texture (one would almost add, in temperature) like stale and still unovened dough become a little soiled in the interval, chewing tobacco with a slow, deliberate, almost cowlike motion of his lower jaw [; ruminant, unwinking, and timeless, <and apparently ageless canceled> written in] he is as symbolic of his <time canceled> age and place as his

	disturbing image of his silk clad wife [talking for a moment struck out] apparently
	passing the [casual marginal addition] time of day with Colonel Hoxey in front of the
	post office.
	the new plate glass window of his recently remodeled bank, while his opaque
	expressionless gaze contemplates with complete inscrutability the buxom and still
	5 Father Abraham carbon typescript (University of Virginia accession number 9817-a.15; WFM 2: 135). The
	corresponding passage in the manuscript reads: " . . . and then Henry's wife came into the room with her
	desolate dog's eyes and stood at the foot of the bed with her worn hands clasped across her shapeless lank
	garment and her face in shadow." (Father Abraham 60, WFM 2: 19-20) After he gave up the typescript of
	Father Abraham but before he wrote the novel As I Lay Dying, Faulkner drafted two versions of the spotted
	horses episode entitled "As I Lay Dying." The source of this title, according to Joseph Blotner, is a fragment
	of an unidentified translation of The Odyssey which Faulkner quoted from memory as "As I lay dying the
	woman with the dog's eyes would not close my eyelids for me as I descended into Hades." (Blotner 634-5,
	91). An italicized passage of "Carcassonne" seems to corroborate this association, if it is remembered that
	Agamemnon is the head of the Greek coalition of kings in Homer's epic: "where fell where I was King of
	Kings but the woman with the woman with the dog's eyes to knock my bones together and together" (CS 898).

predecessor, a portly man [in a tail coat written in] with a sweeping moustache and a strawberry tie and broad soft hat, was of his time and place.

6

  . In the last lines of the section, one intermediate typescript fragment shows Faulkner working in a last handwritten reference to tobaccochewing, making it the characterizing feature of the whole Snopes species: "a month or so after the wedding [there appeared behind the counter in Jody Varner's store a quiet uncommunicative youth with eyes the color of pond water in August and snuff stains at the Before moving on to the further characterization of Flem's mouth, it is worth noting that Faulkner has no other character than Suratt use the phrase "sewed up tighter that an pocketbook." We know that he should know about sewing, because he is a professional. But he has been a professional only since that day when Faulkner corrected the first mention of V K Suratt in pg. 8 of the Father Abraham manuscript and canceled "patent medicine drummer" to replace it with "sewing machine agent." It is as if Suratt and Ratliff had been spawn by Flem and his seam of a mouth, or as if Faulkner had been alerted by the word "patent," also in the phrase describing Flem's mouth, and had found that Suratt's unusual specialty was more fruitful if it echoed the seam of Flem's mouth. This is a good example of the way the vehicle in Faulkner's metaphor gives shape to its tenor: as we shall see, this interest for the tailor or seamstress's art will be put to good thematic use in The Hamlet.The provisional conclusion on this evolution is that when he first appears in The Hamlet Flem just chews. It would be easy enough to conclude that Faulkner has worked hard at smoothing down the rough edges of the fantastic imagery which inspired his developments, just as he clipped the rough edges of his peasants' dialect. But here is a new turn of the metaphorical

corner of his mouth; . . . ms marginal addition]"

(WFM 2: 146)

In the 19-page-long typescript of "As I Lay Dying" one of the initial attempts to mine the Father Abraham material for short stories, Faulkner has Suratt remark on Flem's constantly closed mouth, which he now associates with his strict business sense, as if picking up for the first time the general consumption implication of the phrase "patent tobacco pouches": "Jest look at him settin ther, whittlin that ere stick, with that ere mouth sewed up tighter'n a pocket-book." (WFM 15.1: 5) References to his tobacco addiction are not absent, but it is the spitting that becomes emphasized, a living metaphor of Flem's rejection of social manners and the use of the mouth for speech: "Jest look at him, fellers. After a while he's gwine to open that mouth long enough to spit, maybe."

(WFM 15.1: 9)

In the last sentence of this version he is symbolically on the selling side of the tobacco trade: "I bet Flem could buy that hoss back fum her fer a bottle of snuff too."

(WFM 15.1: 17)

screw: just what Flem chews Faulkner is very careful not to assert. In Flem's first meeting with Jody Varner, at the end of chapter 1 of The Hamlet Varner offers him a cigar. Flem declines: "I dont use them," the other said.

"Just chew, hah?" Varner said.

"I chew up a nickel now and then until the suption is out of it. But I aint never lit a match to one yet."

(H 26)

  To serve as epilogue to this short natural history of Flem's pouch of a mouth, let us look at what happens to it in the sequels to The Hamlet. In The Town Flem uses "an old-fashioned snap-mouth wallet." (246). The Mansion is prosaic, recapitulative as always. In this late novel Ratliff gives away just what, in his opinion, Flem had been chewing: "that same little mouthsized chunk of air he had been chewing ever since he quit tobacco when he finally got to Jefferson and heard about chewing gum and then quit chewing gum too when he found out folks considered the vice-president of a bank rich enough not to have to chew anything." (The

	Mansion

155-6) Chick Mallison gathered that he was even more of an ascetic: "Old Snopes neither smoked nor drank nor even chewed tobacco; what his jaws worked stdily on was, as Ratliff put it, the same little chunk of Frenchman's air he had brought in his mouth when he moved to Jefferson thirty years ago." (220) With this concluding report the truth about Flem's chewing has obviously come full circle. * Another animal metaphor was implied in the earliest references to Flem's chewing: the ruminant. As one would expect this is another recurring essential motif in The Hamlet.

  Clothing behaves in fantastic ways in the world of Frenchman's Bend: think of the garments that Jody Varner hands down to his servants so his suits haunt all the roads of the land he owns simultaneously; of the federal officers' uniforms which disappear and reappear worn by hill-billies in the back country; of the cleated shoes that first draw Varner's attention to Labove's family. Ratliff's expert eye can immediately detect that Flem's shirts are new, that the creases show that they have been sitting on the shelves of a store all summer. Flem's tie is also semantically eloquent:

*

Lastly let us come back to the role of clothing in The Hamlet, which is linked to Suratt's 1/3/1998 5:16:00 PM occupation.

Before the "Rowan Oak papers" were acquired by the University of Mississippi, Michel Gresset called my attention to this holograph fragment, which was then listed as item 9817-5 in the Faulkner collections at the University of Virginia and erroneously described as "Sartoris: description of Flem Snopes sitting in the window of his bank." The fragment is listed, but not reproduced in WFM 2.

The Hamlet 24; henceforth, H in the text.1/3/1998 5:16:00 PM

The comparison between food and money may have originated in Faulkner's reading of Balzac's "Monsieur Gobseck": the name of Balzac's usurer sounds like the colloquial "gobe sec," meaning gluttonous.

See Creighton, William Faukner's Craft of Revision; Pothier, "Note sur le texte" in Faulkner, Oeuvres romanesques III(Paris: Gallimard Pléiade, 2000),

1064-1070.

" (Faulkner in the University