trocr base handwritten

Introduction

The TrOCR model, developed by Microsoft and fine-tuned on the IAM handwriting dataset, is designed for optical character recognition (OCR) of handwritten text. It utilizes a Transformer-based architecture, combining a vision encoder and a text decoder.

Architecture

The TrOCR model employs an encoder-decoder architecture. The image encoder is a Transformer initialized with BEiT weights, while the text decoder is initialized with RoBERTa weights. Images are divided into 16x16 patches, embedded linearly, with absolute position embeddings added. These embeddings are input to the Transformer encoder, and the text decoder generates tokens autoregressively.

Training

The model is fine-tuned on the IAM handwriting dataset. TrOCR is intended primarily for OCR tasks involving single text-line images. The model's performance and applications can be expanded through further fine-tuning for specific tasks.

Guide: Running Locally

To run the TrOCR model locally using PyTorch:

1. Install Dependencies: Ensure you have transformers, PIL, and requests installed.
2. Load Image: Use an image from the IAM database or any other suitable source.
3. Initialize Model and Processor:

   from transformers import TrOCRProcessor, VisionEncoderDecoderModel
   from PIL import Image
   import requests
   
   # Load image
   url = 'https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'
   image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
   
   # Initialize processor and model
   processor = TrOCRProcessor.from_pretrained('microsoft/trocr-base-handwritten')
   model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-base-handwritten')
   
   # Process image
   pixel_values = processor(images=image, return_tensors="pt").pixel_values
   
   # Generate text
   generated_ids = model.generate(pixel_values)
   generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
   

4. Cloud GPUs: Consider using cloud GPUs from providers like AWS, GCP, or Azure for enhanced processing power, especially for large-scale tasks.

License

The model and its code are subject to the licensing terms provided by Microsoft and Hugging Face. Users should review these terms to ensure compliance with their use case.