Tibetan line OCR model (323.24M parameters, VIT encoder + TROCR decoder in a VisionEncoderDecoder architecture) fine-tuned for traditional Tibetan pecha scans with PechaBridge.

Important: This checkpoint expects PechaBridge's gray preprocessing followed by a fixed 256×1024 resize. The gray pipeline uses the minimum RGB channel as grayscale and does not binarize the image. Use the PechaBridge CLI for the supported end-to-end path; raw image input or a generic AutoTokenizer path will not reproduce the training pipeline.

Recommended usage — PechaBridge CLI

bash

git clone https://github.com/CodexAITeam/PechaBridge.git && cd PechaBridge
pip install -r requirements.txt
python cli.py download-models

python cli.py batch-ocr \
    --ocr-model     models/ocr/PechaBridgeOCR \
    --line-model    models/line_segmentation/PechaBridgeLineSegmentation.pt \
    --layout-engine yolo_line \
    --ocr-engine    donut \
    --input-dir     /path/to/pecha/images

Each page image produces a .txt transcript and an *_overlay.jpg preview.

Advanced: standalone Python usage

The tokenizer is backed by BoSentencePiece plus PechaBridge-specific special tokens. Load it with PechaBridge's adapter rather than AutoTokenizer.

python

from pathlib import Path
import torch
from huggingface_hub import snapshot_download
from PIL import Image
from transformers import AutoImageProcessor, VisionEncoderDecoderModel
from pechabridge.ocr.preprocess_bdrc import BDRCPreprocessConfig, preprocess_image_bdrc
from pechabridge.ocr.sentencepiece_tokenizer_adapter import load_sentencepiece_tokenizer

model_dir = Path(snapshot_download("TibetanCodexAITeam/PechaBridgeOCR"))
model = VisionEncoderDecoderModel.from_pretrained(model_dir).eval()
image_processor = AutoImageProcessor.from_pretrained(model_dir, use_fast=False)
tokenizer = load_sentencepiece_tokenizer(model_dir)
image = Image.open('line_crop.png').convert('RGB')
cfg = BDRCPreprocessConfig.vit_defaults()
cfg = BDRCPreprocessConfig.from_dict({
    **cfg.to_dict(), 'binarize': False, 'gray_mode': 'min_rgb',
})
prepared = preprocess_image_bdrc(image=image, config=cfg).convert('RGB')
pixel_values = image_processor(images=prepared, return_tensors='pt').pixel_values
with torch.inference_mode():
    generated_ids = model.generate(pixel_values)
text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(text)

Model details

Checkpoint: checkpoint-184000 (step 184000)
Image preprocessing pipeline: gray
Input geometry: fixed 256×1024 resize, RGB tensor normalized with mean/std 0.5
Architecture: VIT encoder + TROCR decoder, 323.24M parameters
Repro configuration included: yes — preprocessing, generation, normalization, and metric definitions are in repro/
Training framework: PechaBridge
Training data: Tibetan pecha line images from OpenPecha and BDRC collections

Evaluation

Checkpoint	Internal validation CER	Valid samples
`checkpoint-184000`	0.5751%	241

CER is the global character edit distance divided by total normalized reference length. This is a training-time internal validation result, not an independent cross-collection benchmark.

External multi-source evaluation

On a separate 3,306-sample evaluation set, mean CER was 0.80%. 1.63% of samples exceeded 10% CER and 0.57% exceeded 20% CER.

Source dataset	Samples	Mean CER
OCR-Norbuketaka	2,350	0.43%
OCR-Google_Books	812	1.59%
OCR-Lhasakanjur	118	1.92%
OCR-Drutsa	14	3.54%
OCR-Betsug	12	3.86%

This externally supplied result summary was not independently reproduced from artifacts included in this release.

Intended use and limitations

Intended for OCR of individual Tibetan pecha text-line crops.
Page-level use requires a separate line-segmentation/layout stage.
Performance may degrade on other scripts, modern book layouts, handwriting, severe blur, unusual scan colors, or collections not represented during training.
Outputs should be reviewed before scholarly, archival, or other high-impact use.

License

MIT. See the PechaBridge repository for the project license.