A tailored Handwritten-Text-Recognition System for the Middle Latin Dictionary Project at the Bavarian Academy of Sciences and Humanities

How to Install and Run the Main App?

The end-to-end pipeline, which is pip-installable can be found in the Lectiomat folder. Navigate to the Lectiomat folder (cd Lectiomat) and run pip install ..

Run the app after installation in python:

from lectiomat import Lectiomat
lectio = Lectiomat()

MLW Dictionary

• Annotation of ~ 114k data points for training of an object detection model using OFA (Wang et al., 2022)
• Training of a YOLOv8 model for object detection to extract the lemmas
• Training of a HTR model based on the transformer architecture
• Multiple experiments to obtain the best model (CER 0.015, SWIN + GPT-2)
• Lectiomat library (mlw-lectiomat) for the bavarian academy of sciences and humanities

Project Organization

├── LICENSE
├── Makefile           <- Makefile including shortcuts like `make train-ocr` or `make eval`.
├── README.md          <- This file.
├── .gitignore         <- Version management blacklisting.
├── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io
├── requirements.txt   <- The requirements.txt file including all dependencies.
│
├── config             <- Include config.yaml files for training.
│
├── data               <- All data for this project.
│
├── paper-package      <- Pip-installable app for the using our models. Another README is provided in this folder.
│
├── mlw-data           <- Files for downloading the data.
│
├── models             <- Trained models and tokenizer.
│
├── notebooks          <- Notebooks, used throughout the project.
│
├── src                <- Source code for use in this project. Another README is provided in this folder.
│
└── tests              <- Tests for the MLW dataset.

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Project based on the cookiecutter data science project template. #cookiecutterdatascience