iam_handwriting_ocr

Introduction

The ESPnet IAM Handwriting OCR model is developed using the ESPnet framework and is designed for image-to-text tasks, specifically for optical character recognition (OCR) and handwriting recognition. This model is trained using the IAM dataset and is suitable for recognizing handwritten English text.

Architecture

The model architecture leverages ESPnet's capabilities, which include advanced speech and text processing techniques. It incorporates a conformer-based encoder and a transformer-based decoder, allowing it to effectively process and recognize handwritten text from images.

Training

The training process for the ESPnet IAM Handwriting OCR model follows a specific configuration using the ESPnet toolkit. It employs a conformer encoder with 12 blocks and a transformer decoder with 6 blocks. The training uses Adam optimizer with a learning rate of 0.002 and includes various dropout techniques to improve generalization. The model's performance is evaluated using metrics like Word Error Rate (WER) and Character Error Rate (CER).

Guide: Running Locally

To run the ESPnet IAM Handwriting OCR model locally, follow these steps:

1. Install ESPnet: Follow the installation guide on the ESPnet GitHub page.
2. Checkout the Required Version:

   cd espnet
   git checkout 2169367022b8939d22005e8cf45a65bb20bc0768
   pip install -e .
   

3. Run the Model:

   cd egs2/iam/ocr1
   ./run.sh --skip_data_prep false --skip_train true --download_model espnet/iam_handwriting_ocr
   

For better performance, particularly with large datasets, consider using cloud GPU services such as AWS, Google Cloud, or Azure.

License

The ESPnet IAM Handwriting OCR model is licensed under the Creative Commons Attribution 4.0 International License (cc-by-4.0), allowing for sharing and adaptation with appropriate credit.