Cuneiform OCR Data Preprocessing for Ebl Project (https://www.ebl.lmu.de/, https://github.com/ElectronicBabylonianLiterature

Data+Code is part of Paper Sign Detection for Cuneiform Tablets from Yunus Cobanoglu, Luis Sáenz, Ilya Khait, Enrique Jiménez.
Data on Zenodoo .

See https://github.com/ElectronicBabylonianLiterature/cuneiform-ocr/blob/main/README.md for overview and general information of all repositories associated with the paper from above.

Installation

• requirements.txt (optionally: includes opencv-python)
• pip3 install torch=="2.0.1" torchvision --index-url https://download.pytorch.org/whl/cpu
• pip install -U openmim
• mim install "mmocr==1.0.0rc5" it is important to use this exact version because prepare_data.py won't work in newer versions (DATA_PARSERS are not backward compatible)
• mim install "mmcv==2.0.0"

Make sure PYTHONPATH is root of repository

See the explanatory video here.

Data

The data was fetched from our api https://github.com/ElectronicBabylonianLiterature/ebl-api/blob/master/ebl/fragmentarium/retrieve_annotations.py.

To update the dataset use previous function to get new fragments, after that run filter to get fragments appliable for the training.

download raw-data and processed-data according to instructions below. This code will use raw-data and processed-data and output the data as in ready-for-training (i.e. icdar2015 and coco2017 format) (see in zenodoo ready-for-training.tar.gz). Directory Structure

data
  processed-data
    data-coco
    data-icdar2015
    detection
    ...
    classification
      data (after gather_all.py
      ...
  raw-data
    ebl
    heidelberg
    jooch
    urschrei-CDP

Data Preprocessing for Text Detection (Predict only Bounding Boxes)

1. Preprocessing Heidelberg Data, all Details in cuneiform_ocr_data/heidelberg/README.md

2. Ebl (our) data in data/raw-data/ebl (generally better to create test set from ebl data because quality is better)

   2.1. Run extract_contours.py with EXTRACT_AUTMOATICALLY=False on data/raw-data/ebl/detection

   2.2. Run display_bboxes.py and use keys to delete all which are not good quality

3. Run select_test_set.py which will select 50 randomly images from data/processed-data/ebl/ebl-detection-extracted-deleted (currently no option to create val set because of small size of dataset)

4. data/processed-data/ebl/ebl-detection-extracted-test has .txt file will names of all images in test set (this will be necessary to create train,test for classification later)

5. Now merge data/processed-data/heidelberg/heidelberg-extracted-deleted and ebl-detection-extracted-train which will be your train set (see data/processed-data/detection, around 295 train and 50 test instances).

6. Optionally. Create Icdar2015 Style dataset using convert_to_icdar2015.py

7. Optionally: Create Coco Style Dataset convert_to_coco.py will create only a test set coco style

Dataformat

image: P3310-0.jp, with gt_P3310-0.txt.

Ground truth contains top left x, top left y, width, height and sign.

Sign followed by ? means it is partially broken. Unclear signs have value 'UnclearSign'.

Example: 0,0,10,10,KUR

Data Preprocessing for Image Classification

1. Fetch Sign Images from https://labasi.acdh.oeaw.ac.at/ using their api in cuneiform_ocr_data/labasi
2. Run classification/cdp/main.py will map data using our ABZ Sign Mapping some signs can't be mapped (should be checked by an assyiriologist for correctness)
3. Run classification/jooch/main.py will map data using our ABZ Sign Mapping some signs can't be mapped (should be checked by an assyiriologist for correctness)
4. Merge data/processed-data/heidelberg/heidelberg and data/raw-data/ebl/ebl-classification to data/processed-data/ebl+heidelberg-classification
5. Split data/processed-data/ebl+heidelberg-classification to data/processed-data/ebl+heidelberg-classification-train and data/processed-data/ebl+heidelberg-classification-test by copying all files which are part of the detection test set using the script move_test_set_for_classification.py
6. Run crop_sign.py on data/processed-data/ebl+heidelberg-classification-train and data/processed-data/ebl+heidelberg-classification-test you can modify crop_signs.py to include/exclude partially broken or UnclearSigns.
7. data/processed-data/classification should contain Cuneiform Dataset JOOCH, ebl+heidelberg/ebl+heidelberg-train, ebl+heidelberg/ebl+heidelberg-test, labasi and urschrei-CDP-processed
8. gather_all.py will gather and finalize the format for training/testing of all the folders from 7. (gather_all.py will create "cuneiform_ocr_data/classification/data" directory with classes.txt which has all classes used for training/testing)

Data Preprocessing for Image (Sign) Classification (Details)

0. Use move_test_set_for_classification.py to extract all images belonging to detection test set for classification
1. Images are cropped from LMU and Heidelberg using crop_signs.py and converted to ABZ Sign List via ebl.txt mapping from OraccGlobalSignList/ MZL to ABZ Number
   • Partially Broken and Unclear Signs can be dealt included/excluded on parameter in script
2. Images from CDP (urschrei-cdp) are renamed using the mapping from the urschrei-repo https://github.com/urschrei/CDP/csvs (look at cuneiform_ocr/preprocessing_cdp)
   • Images are renamed rename_to_mzl.py
   • Images are mapped via urschrei-cdp corrected_instances_forimport.xlsx and custom mapping via convert_cdp_and_jooch.py
3. Cuneiform JOOCH images are not used due to bad quality currently
4. Labasi Project is scraped with labasi/crawl_labasi_page.py (can take very long multiple hours with interruptions) and renamed manually to fit ebl.txt mapping

Acknowledgements/ Citation

• Deep learning of cuneiform sign detection with weak supervision using transliteration alignment https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0243039
  • Annotated Tablets (75 Tablets) https://compvis.github.io/cuneiform-sign-detection-dataset/
  • -> Heidelberg Data
• Towards Query-by-eXpression Retrieval of Cuneiform Signs [https://patrec.cs.tu-dortmund.de/pubs/papers/Rusakov2020-TQX]
  • -> JOOCH Dataset https://graphics-data.cs.tu-dortmund.de/docs/publications/cuneiform/
• Labasi Project https://labasi.acdh.oeaw.ac.at/
• CDP Project https://github.com/urschrei/CDP
• LMU https://www.ebl.lmu.de/

Cite this paper

@article{CobanogluSáenzKhaitJiménez+2024,
url = {https://doi.org/10.1515/itit-2024-0028},
title = {Sign detection for cuneiform tablets},
title = {},
author = {Yunus Cobanoglu and Luis Sáenz and Ilya Khait and Enrique Jiménez},
journal = {it - Information Technology},
doi = {doi:10.1515/itit-2024-0028},
year = {2024},
lastchecked = {2024-06-01}
}