Trocr Sanskrit Ocr

--- license: mit language: - sa - en tags: - ocr - sanskrit - trocr - image-to-text - devanagari datasets: - yzk/veda-ocr-ms pipeline_tag: image-to-text --- # TrOCR Sanskrit OCR Fine-tuned TrOCR model for Sanskrit manuscript OCR (Optical Character Recognition). ## Model Description This model extracts Sanskrit text from manuscript images. It outputs text in IAST (International Alphabet of Sanskrit Transliteration) format. - **Base Model:** microsoft/trocr-base-printed - **Fine-tuned on:** yzk/veda-ocr-ms (~11.7k Sanskrit manuscript images) - **Training:** 2 epochs on Apple M1 Pro (MPS) ## Usage ```python from transformers import TrOCRProcessor, VisionEncoderDecoderModel from PIL import Image # Load model processor = TrOCRProcessor.from_pretrained("Piyush3142/trocr-sanskrit-ocr") model = VisionEncoderDecoderModel.from_pretrained("Piyush3142/trocr-sanskrit-ocr") # OCR inference image = Image.open("sanskrit_manuscript.jpg").convert("RGB") pixel_values = processor(image, return_tensors="pt").pixel_values outputs = model.generate(pixel_values, max_length=256) text = processor.batch_decode(outputs, skip_special_tokens=True)[0] print(text) # IAST output ``` ## Convert IAST to Devanagari ```python from indic_transliteration.sanscript import transliterate, IAST, DEVANAGARI devanagari = transliterate(text, IAST, DEVANAGARI) print(devanagari) ``` ## Training Details | Parameter | Value | |-----------|-------| | Dataset | yzk/veda-ocr-ms | | Train samples | 10,560 | | Test samples | 1,174 | | Epochs | 2 | | Batch size | 4 | | Learning rate | 2e-5 | | CER | ~68% | | WER | ~80% | ## Limitations - Trained on printed Vedic manuscripts - Best performance on similar style texts - May struggle with handwritten or degraded images ## License MIT

Source context: 95 downloads · 1 likes · Pipeline image-to-text · Repo Piyush3142/trocr-sanskrit-ocr