© 2013

Chinese Handwriting Recognition: An Algorithmic Perspective


Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Tonghua Su
    Pages 1-21
  3. Tonghua Su
    Pages 23-48

About this book


This book provides an algorithmic perspective on the recent development of Chinese handwriting recognition. Two technically sound strategies, the segmentation-free and integrated segmentation-recognition strategy, are investigated and algorithms that have worked well in practice are primarily focused on. Baseline systems are initially presented for these strategies and are subsequently expanded on and incrementally improved. The sophisticated algorithms covered include: 1) string sample expansion algorithms which synthesize string samples from isolated characters or distort realistic string samples; 2) enhanced feature representation algorithms, e.g. enhanced four-plane features and Delta features; 3) novel learning algorithms, such as Perceptron learning with dynamic margin, MPE training and distributed training; and lastly 4) ensemble algorithms, that is, combining the two strategies using both parallel structure and serial structure. All the while, the book moves from basic to advanced algorithms, helping readers quickly embark on the study of Chinese handwriting recognition.


Chinese handwriting recognition distributed training machine learning optical character recognition pattern recognition

Authors and affiliations

  1. 1., Computer ScienceHarbin Institute of Technology, ChinaHarbinChina, People's Republic

About the authors

Dr. Tonghua Su has been working in the character recognition field over 10 years. The research group with which Dr. Su has been working released the HIT-MW database, which is now used at over 60 universities/institutes. They are the first group to systematically study the recognition problem of Chinese handwriting and developed the HMM-based recognizer and the PL-MQDF classifier for Chinese handwritten character recognition.

Bibliographic information

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