Online Handwriting Recognition Using Encoder-Decoder Model

Abeer Eisa, Lina Abdalla, Mohanad Ahmed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we propose a system that is capable of recognizing raw online handwritten data. The system consists of an advanced type of neural network known as LSTM (Long Short Term Memory) Encoder-decoder combined with a customized attention mechanism layer. The attention mechanism has greatly enhanced the system performance from a low character level accuracy of 53% to an excellent accuracy of 96%. Moreover, the system involves a segmentation algorithm designed to divide the sentences into segments of lines. For the training and testing we employ the IAM On-Line Handwriting database, the source can be found here [1]. The accuracy can be improved even further by integrating our system with a language model to spell check the outputs.
Original languageEnglish (US)
Title of host publication2019 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE)
PublisherIEEE
ISBN (Print)978-1-7281-1007-3
DOIs
StatePublished - 2019

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