Deciphering Venetian handwriting
Introduction
The cadasters are essential documents which describe the land reliably and precise manner. The shape of the lots, buildings, bridges, and layout of other urban works are meticulously documented and annotated with a unique number. The "Sommarioni" are the key to reading the map as they contain the parcel number (ref to the map), owner's name, toponym, intended use of the land or building, surface area of the land. The Napoleonic cadaster is an official record of the entire French empire. On the 15th of September 1807, a new law gave the order that the French Empire is to be meticulously measured and documented. This allowed the government to tax their subjects for the land that they owned. 1
These documents now allow historians to research the evolution of the city. The cadaster that this project will focus on is the Venetian cadaster created between 1807 and 1816 as a result of the law previously mentioned.
These documents are a very usefull source of information for historians studying these periods of history. Do help them in this task large amounts of historical documents have been dititalized in the scope of the Venice Time Machine project. The "Sommarioni" have been the focus of previous projects that have focused on manually digitalizing the content of these records. Sadly will doing this process the link to the page that contained the information was lost. A previous attempt to reestablish this link was done in the Mapping Cadasters, DH101 project. The motivation of this project was to attempt to improve the result that they obtained by trying a new approach.
The goal of this project is to create a pipeline that allows reestablishing the mapping between the digital scans of the "Sommarioni" and the digital version of it as an excel spreadsheet. To produce this pipeline, a mix of a deep neural network for handwriting recognition, a cycleGAN to extract patches, and classical image processing technics, and unsupervised machine learning.
Planning
| Week | Task |
|---|---|
| 09 | Segment patch of text in Sommarioni : (page id, patch) |
| 10 | Mapping transcription (excel file) -> page id (proof of concept) |
| 11 | Mapping transcription (excel file) -> page id (on the whole dataset) |
| 12 | Depending of the quality of the results : improve the mapping of page id, more precise matching, viewer web |
| 13 | Final results, final evaluation & final report writing |
| 14 | Final project presentation |
Week 09
- Input : Sommarioni images
- Output : Patch of pixels containing text with coordinate of the patch in the Sommarioni
- Step 1 : Segment hand written text regions in Sommarioni images
- Step 2 : Extraction of the patches
Week 10
- Input : transcription (Excel File), tuples (page id, patch) extracted in week 9
- Output : line in the transcription -> page id
- Step 1 : HTR recognition in the patch and cleaning : (patch, text)
- Step 2 : Find matching pair between recognized text and transcription
- Step 3 : New excel file with the new page id column
Week 11
- Step 1 : Apply the pipeline validated on week 10 on the whole dataset
- Step 2 : Evaluate the quality and based on that decide of the tasks for the next weeks
Week 12
- Depending of the quality of the matching
- Improve image segmentation
- More precise matching (excel cell) -> (page id, patch) in order to have the precise box of each written text
- Use an IIF image viewer to show the results of the project in a more fancy way
Methodology
The project can be summarized as a 4 steps pipeline as shown on figure TODO figue pipeline.
Step 1 - Text detection
The first part of the project consisted of extracting the areas on the image of the page that contains the text. This is a required step since our handwriting recognition model requires as an input a single line of text. To extract the patches the first step is to identify the location of the text on the page. This information will be stored in the standard image metadata description called PAGE. The location will be stored by storing the baseline under some text. To extract the baseline we used the P2PaLA repository. It is a Document Layout Analysis tool based on the pix2pix and CycleGAN. More information on the network being used can be found in the Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks paper. Since there is no ground truth for the locations of baselines in the Sommarioni dataset we used the pre-trained model provided with P2PaLA. Due to the lack of ground truth, we do not have a metric to measure the quality of this step of our pipeline. We conducted a qualitative visual inspection of results. The output is remarkably good given that it was not trained on data from this dataset. A few false positives were found, but no false negatives.
Step 2 - Patch extraction
Once the baselines are identified we need to extract the areas that contained text (cropping the image of the page to contain only a single sentence). No preexisting tool satisfied the quality requirements that we wanted. Therefore, we created PatchExtractor. PatchExtractor is a python program that extracts the patches using as input the source image and the baseline file produced by P2PaLA. PatchExtractor uses some advanced image processing to extract the columns from the source image. The information about the column that the patch is located in, is crucial. After HTR we can use the location information to match the columns of the spreadsheet with their equivalent column in the picture.
The advanced column extractor can produce a clean binary mask of the location of the columns as seen in Fig. 3. To achieve this multiple steps including some such as:
- Applying a Gabor kernel (linear feature extractor)
- Using a contrast threshold
- Connected component size filtering (removing small connected components)
- Gaussian blur
- Affine transformations
- Bit-wise operations
- Cropping
were done to transform the original picture into a column mask of the region of interest (ROI). The ROI is the part of the page that contains the text without the margins.
This mask is then used to identify which column a baseline is in.
There is an extra challenge that we can fix with the knowledge of the location of the columns. Sometimes the baseline detection detects a single baseline for 2 columns that are close to each other as can be seen in Fig. 4
With the knowledge of the location of the columns PatchExtractor fixes these issues and produces 2 distinct images as can be seen in Fig. 5
The output of PatchExtract will produce a patch per column and row containing text, as well as identifying to column in which the patch was extracted. The resulting output can then begin the pre-processing for the HTR.
Step 3 - Handwritten Text Recognition
The third step of the pipeline is the handwritten text recognizer(HTR system. It takes as input the patches extracted during step 2 and produces the text.
The HTR is a deep learning model. The deep learning architecture chosen is the PyLaia architecture that is a mix of Convolutional and 1D recurrent layers based on "Are Multidimensional Recurrent Layers Really Necessary for Handwritten Text Recognition?" (2017) by J. Puigcerver [3].
We did not change the PyLaia architecture but we took it as a framework to train a new model. We first pre-train a model on the IAM dataset[5]. The IAM dataset is a gold standart dataset for HTR. Then we took the model as starting point to train on a specific dataset made from hand-written text from venice. This process is called transfer learning. To evaluate our models we used two standards metrics for HTR systems. We used the Word Error Rate (WER) and the Character Error Rate (CER).
We applied some pre-processing before feeding the patches to the HTR model. We used the pre-processing techniques that are used in PyLaia. The first thing is to enhance the image. It is done ba applying some traditional computer vision techniques to remove the background and clean the patches. Then we resize the patches in order that each patch have the same height.
Then we can feed the patches in our system. These pre-processing steps are used before training but also on the patched extracted in step 2 when we use the HTR. We save the output in csv files containing 2 columns : the name of the patch and the text recognized by the HTR.
Step 4 - Sommarioni matching
The fourth step consists of the actual matching. It takes as input the text recognized by the HTR with some data about the patch itself coming from the step 2 and the excel file containing the transcription. The goal of this step is to establish a mapping between the images and the excel file.
The main challenges comes from the fact that there are errors and inconsistencies from every previous steps that we need to correct or mitigate. This is is during the implementation of this part that we really realized some special cases that are in the Sommariani. The detected special cases are the following :
- Coverta, anteporta and incipit
- Blank tables : tables that contain no data.
- Special tables
The errors and inconsistencies from the previous steps are the following :
- Columns numbers errors (from step 2)
- HTR errors (from step 3)
Coverta, anteporta and incipit are simple to handle the naming of the images of the Sommarionni allow us to just remove them and not take them into account. For the blank tables, the special tables and for the column numbers errors we take a statistical approach to handle them. The valid tables are really similar and the tables being a way to structure information, we can indeed see patterns in our representation of pages. From step 2, we have the column numbers of each patch, and from step 3 we have string of characters for each patch. Taking those information we make feature vectors representing a page. The dimension of the feature vector is equal to the number of columns detected in the page. Each component is equal to the average length of the string of characters for this column in this page.
The blank page, special pages have clearly a different distribution of average string length per column. For error of columns this is more tricky to detect. The errors of columns are often a complete shift of the column. A shift to the right occurs when there is an extra column detected in the left of the image. This could be because in the photograph we can see something else in the left. A shift to the left is the opposite. It is when the first column detected is directly the first column of data.
Quality assessment
License
All contributions that we have done on the project have been granted a MIT license.