trocr large handwritten

Introduction

The TrOCR model, developed by Microsoft, is a large-sized transformer-based model fine-tuned for optical character recognition (OCR) on handwritten text using the IAM dataset. It is designed to convert images of handwritten text into machine-readable text. The model leverages pre-trained weights from BEiT for its image encoder and RoBERTa for its text decoder.

Architecture

TrOCR employs an encoder-decoder architecture. The encoder is an image transformer initialized with BEiT weights, and the decoder is a text transformer initialized with RoBERTa weights. The model processes images by breaking them into fixed-size patches (16x16 pixels), which are linearly embedded and augmented with absolute position embeddings before being fed into the transformer layers. The text decoder then autoregressively generates text tokens.

Training

The model was fine-tuned on the IAM handwritten text dataset. It is primarily intended for OCR applications on single-line text images and can be further fine-tuned for specific tasks or datasets.

Guide: Running Locally

To use TrOCR for OCR tasks on handwritten text images in a local environment with PyTorch, follow these steps:

1. Install Required Libraries:

   pip install transformers torch torchvision
   

2. Load and Preprocess an Image:

   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")
   

3. Load the Model and Processor:

   from transformers import TrOCRProcessor, VisionEncoderDecoderModel
   
   processor = TrOCRProcessor.from_pretrained('microsoft/trocr-large-handwritten')
   model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-large-handwritten')
   

4. Generate Text from Image:

   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]
   print(generated_text)
   

For improved performance and faster processing, consider using a cloud GPU service like AWS, Google Cloud, or Azure.

License

The licensing terms for using the TrOCR model are not explicitly detailed in the provided information. Users should refer to Microsoft's official terms or the licensing details on the Hugging Face model page for more information.