Chinese Handwriting Recognition: An Algorithmic Perspective [electronic resource] / by Tonghua Su.

By: Su, Tonghua [author.]Contributor(s): SpringerLink (Online service)Material type: TextTextLanguage: English Series: SpringerBriefs in Electrical and Computer Engineering: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XI, 124 p. 62 illus., 16 illus. in color. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642318122Subject(s): Computer science | Computer vision | Optical pattern recognition | Computer Science | Computer Imaging, Vision, Pattern Recognition and Graphics | Pattern Recognition | Image Processing and Computer VisionAdditional physical formats: Printed edition:: No titleDDC classification: 006.6 LOC classification: T385TA1637-1638TK7882.P3Online resources: Click here to access online
Contents:
Introduction -- HIT-MW Database -- Integrated Segmentation-Recognition Strategy -- Segmentation-free Strategy: Basic Algorithms -- Segmentation-free Strategy: Advanced Algorithms.
In: Springer eBooksSummary: 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.
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Introduction -- HIT-MW Database -- Integrated Segmentation-Recognition Strategy -- Segmentation-free Strategy: Basic Algorithms -- Segmentation-free Strategy: Advanced Algorithms.

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.

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