ElectronicBabylonianLiterature / ebl-ai-api

ebl-ai-api

Ebl Ai Api

Server deploying a deep learning model for inference on detecting bounding boxes on cuneiform sign tablets For training please refer to cuneiform-sign-detection repo

Table of contents

• Setup
• Codestyle
• Running the tests
• Running the application
• Acknowledgements

Setup

Requirements:

• sudo apt-get install ffmpeg libsm6 libxext6  -y  
  (may be needed for open-cv python)

• Python 3.9

python3 -m venv ./.venv

pyre-configuration specifies paths specifically to .venv directory

pip3 install -r requirements

Run

python3 ebl_ai/check_installation.py

to check pytorch, mmcv, mmdet and mmocr installation.

Model

• Using FCENet (CVPR'2021)
• FCENet implementation: MMOCR
• FCENET with deconvolutions has slightly better performance.
• FCENET without deconvolutions.
• We use FCENET without deconvolutions and with Resnet-18 as Backbone (checkpoint and config specified in ./model directory)

Running the tests

• Use command black ebl_ai_api to format code.
• Use command flake8 for linting.
• Use command pytest to run all tests.
• Use command pyre check for type-checking.

Running the server

waitress-serve --port=8001 --call ebl.app:get_app

Acknowledgements

• FCENET https://arxiv.org/abs/2104.10442
• Using https://github.com/open-mmlab/mmocr/blob/main/configs/textdet/fcenet/README.md (CVPR'2021)
• MMOCR https://github.com/open-mmlab/mmocr
• Deep learning of cuneiform sign detection with weak supervision using transliteration alignment https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0243039
• Synthetic Cuneiform Dataset (2000 Tablets) from https://github.com/cdli-gh/Cuneiform-OCR
• Annotated Tablets (75 Tablets) https://compvis.github.io/cuneiform-sign-detection-dataset/