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The first monograph available on the recognition of Chinese handwriting texts
A systematic sampling mechanics is presented for Chinese handwriting
A further reading list with literature analysis is devoted to help readers quickly overview the state-of-the-art
Include scalable learning methods for large category learning tasks with inspiring results
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.
Content Level »Research
Keywords »Chinese handwriting recognition - distributed training - machine learning - optical character recognition - pattern recognition