A deformable elastic matching model for handwritten Chinesecharacter recognition

Conference Theme: Intelligent Systems for the 21st CenturyThis paper describes a deformable elastic matching approach to handwritten Chinese character recognition (HCCR). Handwritten character is regarded as a kind of deformable object, with elastic property. For the same category of character, we assume that different handwriting variations share the same topological structure, but may differ in shape details. The variations between different handwriting characters are modelled by a set of stroke displacement vectors (SDV). According to the SDV derived, a model character is deformed gradually, in an effort to transform itself much closer to an input character. Experiments show that the proposed elastic matching model can efficiently deal with local shape changes and variations between characters.published_or_final_versio

On-line Chinese character recognition.

by Jian-Zhuang Liu.Thesis (Ph.D.)--Chinese University of Hong Kong, 1997.Includes bibliographical references (p. 183-196).Microfiche. Ann Arbor, Mich.: UMI, 1998. 3 microfiches ; 11 x 15 cm

Off-Line Handwritten Arabic Characters Segmentation Using Slant-Tolerant Segment Features (STSF) [PJ6123. S562 2007 f rb].

Tema utama bagi kajian ini ialah pensegmenan aksara tulisan Arab luar talian. Suatu sistem pengecaman aksara tulisan Arab yang baik mampu meningkatkan kesalingtindakan antara manusia dengan komputer. The main theme of this research is the off-line handwritten Arabic characters segmentation. A successful handwritten Arabic character recognition system improves interactivity between the human and the computers

Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

Four cornered code based Chinese character recognition system.

by Tham Yiu-Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 1993.Includes bibliographical references.Abstract --- p.iAcknowledgements --- p.iiiTable of Contents --- p.ivChapter Chapter I --- IntroductionChapter 1.1 --- Introduction --- p.1-1Chapter 1.2 --- Survey on Chinese Character Recognition --- p.1-4Chapter 1.3 --- Methodology Adopts in Our System --- p.1-7Chapter 1.4 --- Contributions and Organization of the Thesis --- p.1-11Chapter Chapter II --- Pre-processing and Stroke ExtractionChapter 2.1 --- Introduction --- p.2-1Chapter 2.2 --- Thinning --- p.2-1Chapter 2.2.1 --- Introduction to Thinning --- p.2-1Chapter 2.2.2 --- Proposed Thinning Algorithm Cater for Stroke Extraction --- p.2-6Chapter 2.2.3 --- Thinning Results --- p.2-9Chapter 2.3 --- Stroke Extraction --- p.2-13Chapter 2.3.1 --- Introduction to Stroke Extraction --- p.2-13Chapter 2.3.2 --- Proposed Stroke Extraction Method --- p.2-14Chapter 2.3.2.1 --- Fork point detection --- p.2-16Chapter 2.3.2.2 --- 8-connected fork point merging --- p.2-18Chapter 2.3.2.3 --- Sub-stroke extraction --- p.2-18Chapter 2.3.2.4 --- Fork point merging --- p.2-19Chapter 2.3.2.5 --- Sub-stroke connection --- p.2-24Chapter 2.3.3 --- Stroke Extraction Accuracy --- p.2-27Chapter 2.3.4 --- Corner Detection --- p.2-29Chapter 2.3.4.1 --- Introduction to Corner Detection --- p.2-29Chapter 2.3.4.2 --- Proposed Corner Detection Formulation --- p.2-30Chapter 2.4 --- Concluding Remarks --- p.2-33Chapter Chapter III --- Four Corner CodeChapter 3.1 --- Introduction --- p.3-1Chapter 3.2 --- Deletion of Hook Strokes --- p.3-3Chapter 3.3 --- Stroke Types Selection --- p.3-5Chapter 3.4 --- Probability Formulations of Stroke Types --- p.3-7Chapter 3.4.1 --- Simple Strokes --- p.3-7Chapter 3.4.2 --- Square --- p.3-8Chapter 3.4.3 --- Cross --- p.3-10Chapter 3.4.4 --- Upper Right Corner --- p.3-12Chapter 3.4.5 --- Lower Left Corner --- p.3-12Chapter 3.5 --- Corner Segments Extraction Procedure --- p.3-14Chapter 3.5.1 --- Corner Segment Probability --- p.3-21Chapter 3.5.2 --- Corner Segment Extraction --- p.3-23Chapter 3.6 4 --- C Codes Generation --- p.3-26Chapter 3.7 --- Parameters Determination --- p.3-29Chapter 3.8 --- Sensitivity Test --- p.3-31Chapter 3.9 --- Classification Rate --- p.3-32Chapter 3.10 --- Feedback by Corner Segments --- p.3-34Chapter 3.11 --- Classification Rate with Feedback by Corner Segment --- p.3-37Chapter 3.12 --- Reasons for Mis-classification --- p.3-38Chapter 3.13 --- Suggested Solution to the Mis-interpretation of Stroke Type --- p.3-41Chapter 3.14 --- Reduce Size of Candidate Set by No.of Input Segments --- p.3-43Chapter 3.15 --- Extension to Higher Order Code --- p.3-45Chapter 3.16 --- Concluding Remarks --- p.3-46Chapter Chapter IV --- RelaxationChapter 4.1 --- Introduction --- p.4-1Chapter 4.1.1 --- Introduction to Relaxation --- p.4-1Chapter 4.1.2 --- Formulation of Relaxation --- p.4-2Chapter 4.1.3 --- Survey on Chinese Character Recognition by using Relaxation --- p.4-5Chapter 4.2 --- Relaxation Formulations --- p.4-9Chapter 4.2.1 --- Definition of Neighbour Segments --- p.4-9Chapter 4.2.2 --- Formulation of Initial Probability Assignment --- p.4-12Chapter 4.2.3 --- Formulation of Compatibility Function --- p.4-14Chapter 4.2.4 --- Formulation of Support from Neighbours --- p.4-16Chapter 4.2.5 --- Stopping Criteria --- p.4-17Chapter 4.2.6 --- Distance Measures --- p.4-17Chapter 4.2.7 --- Parameters Determination --- p.4-21Chapter 4.3 --- Recognition Rate --- p.4-23Chapter 4.4 --- Reasons for Mis-recognition in Relaxation --- p.4-27Chapter 4.5 --- Introduction of No-label Class --- p.4-31Chapter 4.5.1 --- No-label Initial Probability --- p.4-31Chapter 4.5.2 --- No-label Compatibility Function --- p.4-32Chapter 4.5.3 --- Improvement by No-label Class --- p.4-33Chapter 4.6 --- Rate of Convergence --- p.4-35Chapter 4.6.1 --- Updating Formulae in Exponential Form --- p.4-38Chapter 4.7 --- Comparison with Yamamoto et al's Relaxation Method --- p.4-40Chapter 4.7.1 --- Formulations in Yamamoto et al's Relaxation Method --- p.4-40Chapter 4.7.2 --- Modifications in [YAMAM82] --- p.4-42Chapter 4.7.3 --- Performance Comparison with [YAMAM82] --- p.4-43Chapter 4.8 --- System Overall Recognition Rate --- p.4-45Chapter 4.9 --- Concluding Remarks --- p.4-48Chapter Chapter V --- Concluding RemarksChapter 5.1 --- Recapitulation and Conclusions --- p.5-1Chapter 5.2 --- Limitations in the System --- p.5-4Chapter 5.3 --- Suggestions for Further Developments --- p.5-6References --- p.R-1Appendix User's GuideChapter A .l --- System Functions --- p.A-1Chapter A.2 --- Platform and Compiler --- p.A-1Chapter A.3 --- File List --- p.A-2Chapter A.4 --- Directory --- p.A-3Chapter A.5 --- Description of Sub-routines --- p.A-3Chapter A.6 --- Data Structures and Header Files --- p.A-12Chapter A.7 --- Character File charfile Structure --- p.A-15Chapter A.8 --- Suggested Program to Implement the System --- p.A-1

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

Feature Extraction Methods for Character Recognition

Not Include

Freehand Sketch Recognition for Computer-Assisted Language Learning of Written East Asian Languages

One of the challenges students face in studying an East Asian (EA) language (e.g., Chinese, Japanese, and Korean) as a second language is mastering their selected language’s written component. This is especially true for students with native fluency of English and deficient written fluency of another EA language. In order to alleviate the steep learning curve inherent in the properties of EA languages’ complicated writing scripts, language instructors conventionally introduce various written techniques such as stroke order and direction to allow students to study writing scripts in a systematic fashion. Yet, despite the advantages gained from written technique instruction, the physical presence of the language instructor in conventional instruction is still highly desirable during the learning process; not only does it allow instructors to offer valuable real-time critique and feedback interaction on students’ writings, but it also allows instructors to correct students’ bad writing habits that would impede mastery of the written language if not caught early in the learning process. The current generation of computer-assisted language learning (CALL) applications specific to written EA languages have therefore strived to incorporate writing-capable modalities in order to allow students to emulate their studies outside the classroom setting. Several factors such as constrained writing styles, and weak feedback and assessment capabilities limit these existing applications and their employed techniques from closely mimicking the benefits that language instructors continue to offer. In this thesis, I describe my geometric-based sketch recognition approach to several writing scripts in the EA languages while addressing the issues that plague existing CALL applications and the handwriting recognition techniques that they utilize. The approach takes advantage of A Language to Describe, Display, and Editing in Sketch Recognition (LADDER) framework to provide users with valuable feedback and assessment that not only recognizes the visual correctness of students’ written EA Language writings, but also critiques the technical correctness of their stroke order and direction. Furthermore, my approach provides recognition independent of writing style that allows students to learn with natural writing through size- and amount-independence, thus bridging the gap between beginner applications that only recognize single-square input and expert tools that lack written technique critique