A Computationally Efficient Pipeline Approach to Full Page Offline Handwritten Text Recognition

TL;DR

This paper presents a computationally efficient full page offline handwritten text recognition framework that combines object detection and CNN-LSTM recognition, achieving comparable accuracy with less resource usage.

Contribution

A novel pipeline integrating object detection with CNN-LSTM recognition for full page offline handwritten text, reducing computational costs while maintaining accuracy.

Findings

Achieves comparable error rates to state-of-the-art methods.

Uses less memory and computational time.

Demonstrates potential for deployment in real-world applications.

Abstract

Offline handwriting recognition with deep neural networks is usually limited to words or lines due to large computational costs. In this paper, a less computationally expensive full page offline handwritten text recognition framework is introduced. This framework includes a pipeline that locates handwritten text with an object detection neural network and recognises the text within the detected regions using features extracted with a multi-scale convolutional neural network (CNN) fed into a bidirectional long short term memory (LSTM) network. This framework achieves comparable error rates to state of the art frameworks while using less memory and time. The results in this paper demonstrate the potential of this framework and future work can investigate production ready and deployable handwritten text recognisers.