README.md

metadata

language:
  - bo
license: cc0-1.0
tags:
  - tesseract
  - ocr
  - tibetan
  - uchen
  - fine-tuned
library_name: tesseract
pipeline_tag: image-to-text
datasets:
  - openpecha/OCR-Tibetan_line_to_text_benchmark

bod_uchen — Fine-Tuned Tesseract Model for Tibetan Uchen Script

A fine-tuned Tesseract OCR model for recognizing Tibetan Uchen (དབུ་ཅན་) script, trained on 121K+ line-level image–text pairs from historical Tibetan woodblock prints.

Model Details

Field	Value
Model name	bod_uchen
Base model	bod.traineddata from tessdata_best
Script	Tibetan Uchen (དབུ་ཅན་)
Language	Tibetan (bod)
Engine	Tesseract LSTM
PSM	13 (Raw line)
Epochs	5
Learning rate	0.001
Target error rate	0.005

Training Data

The model was trained on a curated subset of Tibetan OCR line-level images paired with ground-truth text transcriptions, formatted for Tesseract training. The dataset is derived from openpecha/OCR-Tibetan_line_to_text_benchmark and prepared by the bo_tessaract_data_prep pipeline.

Split	Directory	Samples
Train	train-ground-truth/	96,978
Eval (Val)	val-ground-truth/	12,123
Test	test-ground-truth/	12,125
Total		121,226

Training Results

Training converged at iteration 526,010 / 545,515 total:

Metric	Value
Best BCER (selected model)	9.719%
Final BCER (train)	10.690%
Mean RMS	1.077%
Delta	2.498%
Skip ratio	0.000%

Collections Included

Five historical Tibetan text collections are included from the source dataset:

Collection	Description
Lithang_Kanjur	Lithang edition of the Tibetan Buddhist canon (Kangyur)
Derge_Tenjur	Derge edition of the Tengyur commentarial collection
Lhasa_Kanjur	Lhasa edition of the Kangyur
KhyentseWangpo	Writings of Jamyang Khyentse Wangpo
Karmapa8	Texts attributed to the 8th Karmapa, Mikyö Dorje

Usage

Prerequisites

Install Tesseract 5.x:

# macOS
brew install tesseract

# Ubuntu/Debian
sudo apt install tesseract-ocr

Download & Install the Model

Download bod_uchen.traineddata from this repository and place it in your Tesseract tessdata directory:

# Find your tessdata path
tesseract --print-parameters | grep tessdata

# Typical locations:
#   macOS (Homebrew): /opt/homebrew/share/tessdata/
#   Linux:           /usr/share/tesseract-ocr/5/tessdata/

Run OCR

tesseract input_image.png output -l bod_uchen --psm 13

For multi-line page segmentation:

tesseract input_image.png output -l bod_uchen --psm 6

Python (pytesseract)

import pytesseract
from PIL import Image

image = Image.open("tibetan_line.png")
text = pytesseract.image_to_string(image, lang="bod_uchen", config="--psm 13")
print(text)

Training Command

The model was fine-tuned using tesstrain:

make training \
  MODEL_NAME=bod_uchen \
  START_MODEL=bod \
  TESSDATA=data \
  GROUND_TRUTH_DIR=data/bo_tesseract \
  LANG_TYPE=Indic \
  PSM=13 \
  EPOCHS=5 \
  LEARNING_RATE=0.001 \
  TARGET_ERROR_RATE=0.005 \
  WORDLIST_FILE=data/langdata/bod/bod.wordlist \
  NUMBERS_FILE=data/langdata/bod/bod.numbers \
  PUNC_FILE=data/langdata/bod/bod.punc \
  2>&1 | tee -a data/bod_uchen/training.log

Intended Use

• OCR of historical Tibetan woodblock-printed texts in Uchen script
• Digitization of Kangyur, Tengyur, and other classical Tibetan collections
• Building searchable archives of Tibetan Buddhist literature

Limitations

• Optimized for Uchen (དབུ་ཅན་) woodblock print style; may underperform on Umé (དབུ་མེད་) or handwritten Tibetan
• Trained primarily on the five collections listed above; generalization to other print styles or modern typeset Tibetan may vary
• Best results when input images are pre-segmented into individual text lines (PSM 13)

Citation

If you use this model, please cite:

@misc{bod_uchen_tesseract,
  title   = {bod_uchen: Fine-Tuned Tesseract Model for Tibetan Uchen Script},
  author  = {Buddhist Digital Resource Center (BDRC) and Dharmaduta},
  year    = {2025},
  url     = {https://huggingface.co/bdrc/bod_uchen_tesseract},
  note    = {Fine-tuned from tessdata_best/bod.traineddata, funded by the Khyentse Foundation}
}

Acknowledgements

This model was developed by Dharmaduta from specifications provided by the Buddhist Digital Resource Center (BDRC) for the BDRC Etext Corpus, with funding from the Khyentse Foundation.