trocr-prescription – AI Model by shubham879 | AlphaNeural AI

You can deploy this model and start earning money today!

shubham879/
trocr-prescription

like

0

vision-encoder-decoder

image-text-to-text

handwritten-text-recognition

prescription-ocr

microsoft/trocr-base-handwritten

endpoints_compatible

Views

No views yet

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 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.