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Machine Learning Techniques in Handwriting Recognition: Problems and Solutions

Machine Learning Techniques in Handwriting Recognition: Problems and Solutions
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Author(s): Hong Lee (Central Queensland University, Australia), Brijesh Verma (Central Queensland University, Australia), Michael Li (Central Queensland University, Australia)and Ashfaqur Rahman (Central Queensland University, Australia)
Copyright: 2012
Pages: 18
Source title: Machine Learning Algorithms for Problem Solving in Computational Applications: Intelligent Techniques
Source Author(s)/Editor(s): Siddhivinayak Kulkarni (University of Ballarat, Australia)
DOI: 10.4018/978-1-4666-1833-6.ch002

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Abstract

Handwriting recognition is a process of recognizing handwritten text on a paper in the case of offline handwriting recognition and on a tablet in the case of online handwriting recognition and converting it into an editable text. In this chapter, the authors focus on offline handwriting recognition, which means that recognition system accepts a scanned handwritten page as an input and outputs an editable recognized text. Handwriting recognition has been an active research area for more than four decades, but some of the major problems still remained unsolved. Many techniques, including the machine learning techniques, have been used to improve the accuracy. This chapter focuses on describing the problems of handwriting recognition and presents the solutions using machine learning techniques for solving major problems in handwriting recognition. The chapter also reviews and presents the state of the art techniques with results and future research for improving handwriting recognition.

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