Trocr Prescription

--- language: en license: apache-2.0 tags: - trocr - handwritten-text-recognition - medical-ocr - prescription-ocr - vision-encoder-decoder - transformers pipeline_tag: image-to-text library_name: transformers base_model: microsoft/trocr-base-handwritten --- # Model Card for trocr-prescription ## Model Details ### Model Description This model is a fine-tuned version of **microsoft/trocr-base-handwritten** for handwritten medical prescription text recognition. It is designed to extract handwritten prescription text from medical images using transformer-based OCR. - **Developed by:** shubham879 - **Model type:** VisionEncoderDecoderModel - **Language(s):** English - **License:** Apache-2.0 - **Finetuned from model:** microsoft/trocr-base-handwritten ### Model Sources - **Repository:** https://huggingface.co/shubham879/trocr-prescription - **Demo:** https://prescription-error-detection.vercel.app/ --- ## Uses ### Direct Use This model is intended for: - handwritten prescription OCR - medical text extraction - OCR preprocessing pipelines ### Downstream Use Can be integrated into: - prescription error detection systems - medical document digitization - healthcare OCR pipelines ### Out-of-Scope Use Not recommended for: - printed document OCR - multilingual handwriting - severely distorted or noisy scans --- ## Bias, Risks, and Limitations - Accuracy decreases on unclear handwriting - Sensitive to poor image quality - Drug names with unusual handwriting may be misread ### Recommendations Use cropped line-level prescription images for best performance. --- ## How to Get Started with the Model ```python from transformers import TrOCRProcessor, VisionEncoderDecoderModel from PIL import Image processor = TrOCRProcessor.from_pretrained("shubham879/trocr-prescription") model = VisionEncoderDecoderModel.from_pretrained("shubham879/trocr-prescription") image = Image.open("sample.png").convert("RGB") pixel_values = processor(images=image, return_tensors="pt").pixel_values generated_ids = model.generate(pixel_values) text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] print(text) ``` ## Training Details ### Training Data The model was fine-tuned using the **Doctor’s Handwritten Prescription BD dataset**, which contains handwritten medical prescription images. ### Training Procedure #### Preprocessing - Images re...

Source context: 51 downloads · 0 likes · Pipeline image-to-text · Library transformers · Repo shubham879/trocr-prescription