trocr small handwritten

Introduction
The TrOCR (Transformer-based Optical Character Recognition) model, specifically the small-sized variant fine-tuned on the IAM handwriting database, is designed for OCR tasks. Introduced by Li et al. in their paper, TrOCR utilizes pre-trained models for improved accuracy and performance in recognizing text from images.

Architecture
TrOCR is an encoder-decoder model combining an image Transformer and a text Transformer. The image encoder is based on the DeiT model, while the text decoder uses UniLM. Images are processed as sequences of 16x16 pixel patches, which are linearly embedded and combined with absolute position embeddings before being input into the Transformer encoder. The text decoder sequentially generates tokens to produce text output from the image input.

Training
The model has been fine-tuned using the IAM dataset, a widely recognized dataset for handwriting recognition tasks. The fine-tuning process enables the model to accurately perform OCR on single-line text images by learning from handwritten text samples.

Guide: Running Locally
To use the model locally with PyTorch, follow these steps:

1. Install Dependencies:

   • Ensure that Python and PyTorch are installed.
   • Install the transformers library from Hugging Face and the PIL library for image processing.

   pip install transformers
   pip install pillow
   

2. Load the Model and Processor:

   • Use the following code to load a sample image and apply the model.

   from transformers import TrOCRProcessor, VisionEncoderDecoderModel
   from PIL import Image
   import requests
   
   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")
   
   processor = TrOCRProcessor.from_pretrained('microsoft/trocr-small-handwritten')
   model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-small-handwritten')
   pixel_values = processor(images=image, return_tensors="pt").pixel_values
   
   generated_ids = model.generate(pixel_values)
   generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
   

3. Run on Cloud GPUs:

   • For large-scale processing or faster inference, consider using cloud services like AWS, GCP, or Azure, which offer GPU instances.

License
The model and its usage are subject to the licensing terms provided by Microsoft and Hugging Face. Always ensure compliance with these terms when using and deploying the model.