Summary

This repository provides a comprehensive solution for Handwritten Text Recognition (HTR) and synthesis using Tensorflow. It targets researchers and practitioners needing robust tools for processing, training, and deploying HTR models, enhanced by generative capabilities for handwriting synthesis and spelling correction. The integration of MLflow provides robust experiment tracking, aiding reproducibility and model management.

How It Works

The project employs a pipeline approach built on Tensorflow, supporting distinct models for recognition, synthesis, segmentation, and writer identification. A key feature is the integration of generative models that can synthesize realistic handwriting, which can then be used to augment training data for recognition models, addressing data scarcity. MLflow is leveraged for detailed tracking of training and testing phases, logging metrics, and managing model artifacts.

Quick Start & Requirements

Requires Python 3.11+ and pip. Installation involves cloning the repository, creating and activating a virtual environment (python3 -m venv .venv, source .venv/bin/activate), and installing dependencies via pip install -r requirements.txt. A tutorial notebook is available for guided setup and exploration.

Highlighted Details

• Supports a wide array of HTR datasets including IAM, EMNIST, MNIST, and custom datasets like BRESSAY.
• Features extensive data augmentation techniques (Mixup, elastic transformations, noise, blur, etc.) to improve model robustness.
• Integrates MLflow for experiment tracking, enabling logging, comparison, and reproducibility of training runs.
• Offers a combined workflow for handwriting synthesis and recognition, using generated data for augmentation.

Maintenance & Community

The project is actively developed as part of PhD work and is in parallel development. Sponsorship is encouraged via Ko-fi to support further enhancements and feature implementation. No specific community channels (like Discord/Slack) are listed.

Licensing & Compatibility

The license type is not explicitly stated in the provided README. This lack of clarity may pose compatibility issues for commercial use or integration into closed-source projects.

Limitations & Caveats

The project's status as part of ongoing PhD research implies potential for evolving priorities and API changes. The absence of explicit licensing information is a significant adoption blocker, requiring clarification before use in production or commercial environments.