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Advances in Chinese document and text processing

Author: Cheng-Lin Liu; Yue Lu
Publisher: [Hoboken] New Jersey : World Scientific, [2017] ©2017
Series: Series on language processing, pattern recognition, and intelligent systems, v. 2.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
"The book is a collection of invited chapters by experts in Chinese document and text processing, and is part of a series on Language Processing, Pattern Recognition, and Intelligent Systems. The chapters introduce the latest advances and state-of-the-art methods for Chinese document image analysis and recognition, font design, text analysis and speaker recognition. Handwritten Chinese character recognition and text  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
(OCoLC)953806354
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Cheng-Lin Liu; Yue Lu
ISBN: 9789813143685 9813143681
OCLC Number: 981975306
Language Note: In English, with examples in Chinese.
Description: 1 online resource : illustrations.
Contents: Preface; Chapter 1 Characteristics of English, Chinese, and Arabic Typefaces; 1. Introduction; 1.1. The purpose of this chapter; 1.2. Literature review of typeface personality studies; 1.3. Participants; 1.4. Materials; 1.5. Results; 2. Chinese Character Characteristics Compared with English; 2.1. Overview; 2.2. Relative baseline feature; 2.3. Squared character feature; 2.4. Weight detection; 2.5. Serif / sans serif / script; 2.6. Structure design; 2.7. Stroke contrast; 2.8. Design proportion; 2.9. Width to height ratio; 2.10. Character spacing; 2.11. Counter design & inner design. 2.12. Stem and cap height design2.13. Junction points; 2.14. Stroke ending; 2.15. Curve; 3. Comparing Arabic with Latin Font Characteristics; 3.1. Overview; 3.2. Feature of Arabic script compared to Latin script; 3.3. Anatomy of characters; 3.4. Design considerations; References; Chapter 2 Chinese Handwriting Database Building and Benchmarking; 1. Introduction; 2. Data Collection Settings; 2.1. Character sets; 2.2. Data collection; 3. Data Annotation; 3.1. Annotation of offline data; 3.2. Annotation of online data; 3.3. Data format; 4. Statistics of Datasets; 5. Recommendations of Usage. 5.1. Data partitioning5.2. Research scenarios; 5.2.1. Handwritten document segmentation; 5.2.2. Handwritten character recognition; 5.2.3. Text line recognition; 5.2.4. Handwritten document retrieval; 5.2.5. Writer adaptation; 5.2.6. Writer identi cation; 6. Preliminary Evaluation; 7. Competition Results; 8. Conclusion; References; Chapter 3 CNN Based Handwritten Character Recognition; 1. Introduction; 1.1. Development of deep learning; 1.2. CNN for image understanding; 1.3. Character recognition by CNN; 2. Overview of the CNN-Based Handwritten Character Recognition System. 2.1. The framework of the system2.2. Recognition beyond human; 3. Random Distortion for Sample Generation; 4. Training Tricks of CNN Model; 5. Model Scale and Input Image Size; 6. Multi-Model Voting; 7. Conclusion; References; Chapter 4 Online Handwritten Chinese Character Recognition: From a Bayesian Approach to Deep Learning; 1. Introduction; 2. Online HCCR under a Bayesian Statistical Learning Framework; 2.1. General framework for OHCCR; 2.2. Preprocessing; 2.2.1. Normalization; 2.2.2. Data augmentation with deformation transformation; 2.2.3. Imaginary stroke technique. 2.3. Feature extraction2.3.1. Region partition with the meshing technique; 2.3.2. 8-directional feature extraction; 2.3.3. Signature of path features; 2.4. Feature dimension reduction with LDA; 2.5. MQDF classifier; 3. OHCCR-CNN: An End-to-End Approach for OHCCR using Deep Convolutional Neural Networks; 3.1. Brief introduction to CNNs; 3.1.1. Convolutional layer; 3.1.2. Pooling layer; 3.1.3. Softmax layer and loss function; 3.1.4. Platforms for training CNNs; 3.2. Domain knowledge-enhanced DCNN for OHCCR; 3.3. Efficient training of CNN using a new algorithm named DropSample.
Series Title: Series on language processing, pattern recognition, and intelligent systems, v. 2.
Responsibility: edited by Cheng-Lin Liu, Yue Lu.

Abstract:

"The book is a collection of invited chapters by experts in Chinese document and text processing, and is part of a series on Language Processing, Pattern Recognition, and Intelligent Systems. The chapters introduce the latest advances and state-of-the-art methods for Chinese document image analysis and recognition, font design, text analysis and speaker recognition. Handwritten Chinese character recognition and text line recognition are at the core of document image analysis (DIA), and therefore, are addressed in four chapters for different scripts (online characters, offline characters, ancient characters, and text lines). Two chapters on character recognition pay much attention to deep convolutional neural networks (CNNs), which are widely used and performing superiorly in various pattern recognition problems. A chapter is contributed to describe a large handwriting database consisting both online and offline characters and text pages. Postal mail reading and writer identification, addressed in two chapters, are important applications of DIA. The collection can serve as reference for students and engineers in Chinese document and text processing and their applications."--Publisher's website.

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