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
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 resized using TrOCRProcessor
- RGB conversion applied
- Tokenization handled by TrOCRProcessor
Training Hyperparameters
- Training regime: fp32
Evaluation
Metrics
- Character Error Rate (CER)
- Sequence Accuracy
Results
- CER: 9%
- Accuracy: 73%
The model performs well on handwritten prescription text at line level with low character-level error.