SimpleHTR: a handwritten text recognition system that converts images of words and text lines into digital text

What it solves

This project provides a system for Handwritten Text Recognition (HTR), allowing users to convert images of handwritten single words or full text lines into digital text. It addresses the challenge of accurately recognizing handwritten characters and words, particularly when dealing with varying handwriting styles.

How it works

The system is built with TensorFlow and utilizes a neural network architecture consisting of five Convolutional Neural Network (CNN) layers for feature extraction, two Recurrent Neural Network (RNN/LSTM) layers for sequence modeling, and a Connectionist Temporal Classification (CTC) loss and decoding layer to map the visual features to text.

It supports three types of decoding methods:

• Bestpath: The default basic decoder.
• Beamsearch: A more advanced search-based decoder.
• Word Beam Search: An optional integrated decoder that constrains recognized words to a specific dictionary to improve accuracy.

Who it’s for

This tool is designed for developers and researchers interested in optical character recognition (OCR) for handwriting, as well as anyone needing to digitize handwritten documents using the IAM off-line HTR dataset.

Highlights

• Multi-level Recognition: Capable of recognizing both single words and entire lines of text.
• Flexible Decoding: Offers multiple CTC decoders, including a specialized word beam search for dictionary-constrained recognition.
• Performance Optimization: Includes an LMDB-based fast image loader to reduce training bottlenecks.
• Pretrained Models: Provides ready-to-use models for both word-level and line-level recognition.