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RuDialect-HTR: Handwritten Russian Dialectic Text Recognition using CRAFT/YOLO and TrOCR

RuDialect-HTR is a project focused on the detection and recognition of handwritten Russian dialectic texts using modern deep learning models.

Repository Structure

This repository contains four Jupyter notebooks:

  • fine-tuning-craft — fine-tuning the CRAFT model for text detection
  • fine-tuning-yolo — fine-tuning YOLO for text detection
  • fine-tuning-trocr — fine-tuning TrOCR for handwritten text recognition (HTR)
  • htr-pipeline — full end-to-end HTR pipeline: text detection + recognition + output formatting

Training Results

All training was performed in Kaggle Notebooks using free cloud GPUs.

Model Notebook Task
CRAFT CRAFT: Russian Dialectic Text Detection Text Detection
YOLO YOLO: Russian Dialectic Text Detection Text Detection
TrOCR TrOCR: Russian Dialectic HTR with PEFT (DoRA) Handwritten Text Recognition (HTR)

Note: These notebooks are demonstration versions (final experiment configurations). The original development notebooks are private and will not be published.


Model Checkpoints

Fine-tuned model checkpoints are available on Hugging Face:

Model Hugging Face Link
CRAFT Not published due to low performance — weights available in Kaggle notebook output (see above).
YOLO11x-dialectic huggingface.co/Daniil-Domino/yolo11x-dialectic
TrOCR-base-Ru-dialectic huggingface.co/Daniil-Domino/trocr-base-ru-dialectic

Used Datasets

The following datasets were used and are available on Hugging Face:

Dataset Hugging Face Link Task
school_notebooks_RU huggingface.co/datasets/ai-forever/school_notebooks_RU Text Detection
Ru-dialectological-stackmix huggingface.co/datasets/CherryJam/ru-dialectological-stackmix Handwritten Text Recognition (HTR)
Ru-dialectological-fonts-aug huggingface.co/datasets/CherryJam/ru-dialectological-fonts-aug Handwritten Text Recognition (HTR)

Installation & Usage

If you're running Kaggle notebooks listed above, all required dependencies are already included in the environment.

For local or custom environments, install the dependencies manually using:

pip install -r requirements.txt --quiet

Tested with Python version 3.10.18


Contributors

Matvienko Daniil - Machine Learning

Starikov Alexander - Dataset Building, Backend

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Notebooks used for training TrOCR, YOLO and CRAFT models

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