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

Template Based Recognition of On-Line Handwriting

Software for recognition of handwriting has been available for several decades now and research on the subject have produced several different strategies for producing competitive recognition accuracies, especially in the case of isolated single characters. The problem of recognizing samples of handwriting with arbitrary connections between constituent characters (emph{unconstrained handwriting}) adds considerable complexity in form of the segmentation problem. In other words a recognition system, not constrained to the isolated single character case, needs to be able to recognize where in the sample one letter ends and another begins. In the research community and probably also in commercial systems the most common technique for recognizing unconstrained handwriting compromise Neural Networks for partial character matching along with Hidden Markov Modeling for combining partial results to string hypothesis. Neural Networks are often favored by the research community since the recognition functions are more or less automatically inferred from a training set of handwritten samples. From a commercial perspective a downside to this property is the lack of control, since there is no explicit information on the types of samples that can be correctly recognized by the system. In a template based system, each style of writing a particular character is explicitly modeled, and thus provides some intuition regarding the types of errors (confusions) that the system is prone to make. Most template based recognition methods today only work for the isolated single character recognition problem and extensions to unconstrained recognition is usually not straightforward. This thesis presents a step-by-step recipe for producing a template based recognition system which extends naturally to unconstrained handwriting recognition through simple graph techniques. A system based on this construction has been implemented and tested for the difficult case of unconstrained online Arabic handwriting recognition with good results

Chinese information processing

A survey of the field of Chinese information processing is provided. It covers the following areas: the Chinese writing system, several popular Chinese encoding schemes and code conversions, Chinese keyboard entry methods, Chinese fonts, Chinese operating systems, basic Chinese computing techniques and applications

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

Adaptive pattern recognition of hand-written Chinese characters using Fourier transforms of projection profiles

None provided

Features and Algorithms for Visual Parsing of Handwritten Mathematical Expressions

Math expressions are an essential part of scientific documents. Handwritten math expressions recognition can benefit human-computer interaction especially in the education domain and is a critical part of document recognition and analysis. Parsing the spatial arrangement of symbols is an essential part of math expression recognition. A variety of parsing techniques have been developed during the past three decades, and fall into two groups. The first group is graph-based parsing. It selects a path or sub-graph which obeys some rule to form a possible interpretation for the given expression. The second group is grammar driven parsing. Grammars and related parameters are defined manually for different tasks. The time complexity of these two groups parsing is high, and they often impose some strict constraints to reduce the computation. The aim of this thesis is working towards building a straightforward and effective parser with as few constraints as possible. First, we propose using a line of sight graph for representing the layout of strokes and symbols in math expressions. It achieves higher F-score than other graph representations and reduces search space for parsing. Second, we modify the shape context feature with Parzen window density estimation. This feature set works well for symbol segmentation, symbol classification and symbol layout analysis. We get a higher symbol segmentation F-score than other systems on CROHME 2014 dataset. Finally, we develop a Maximum Spanning Tree (MST) based parser using Edmonds\u27 algorithm, which extracts an MST from the directed line of sight graph in two passes: first symbols are segmented, and then symbols and spatial relationship are labeled. The time complexity of our MST-based parsing is lower than the time complexity of CYK parsing with context-free grammars. Also, our MST-based parsing obtains higher structure rate and expression rate than CYK parsing when symbol segmentation is accurate. Correct structure means we get the structure of the symbol layout tree correct, even though the label of the edge in the symbol layout tree might be wrong. The performance of our math expression recognition system with MST-based parsing is competitive on CROHME 2012 and 2014 datasets. For future work, how to incorporate symbol classifier result and correct segmentation error in MST-based parsing needs more research

New trends on digitisation of complex engineering drawings

Engineering drawings are commonly used across different industries such as oil and gas, mechanical engineering and others. Digitising these drawings is becoming increasingly important. This is mainly due to the legacy of drawings and documents that may provide rich source of information for industries. Analysing these drawings often requires applying a set of digital image processing methods to detect and classify symbols and other components. Despite the recent significant advances in image processing, and in particular in deep neural networks, automatic analysis and processing of these engineering drawings is still far from being complete. This paper presents a general framework for complex engineering drawing digitisation. A thorough and critical review of relevant literature, methods and algorithms in machine learning and machine vision is presented. Real-life industrial scenario on how to contextualise the digitised information from specific type of these drawings, namely piping and instrumentation diagrams, is discussed in details. A discussion of how new trends on machine vision such as deep learning could be applied to this domain is presented with conclusions and suggestions for future research directions

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

A novel approach to handwritten character recognition

A number of new techniques and approaches for off-line handwritten character recognition are presented which individually make significant advancements in the field. First. an outline-based vectorization algorithm is described which gives improved accuracy in producing vector representations of the pen strokes used to draw characters. Later. Vectorization and other types of preprocessing are criticized and an approach to recognition is suggested which avoids separate preprocessing stages by incorporating them into later stages. Apart from the increased speed of this approach. it allows more effective alteration of the character images since more is known about them at the later stages. It also allows the possibility of alterations being corrected if they are initially detrimental to recognition. A new feature measurement. the Radial Distance/Sector Area feature. is presented which is highly robust. tolerant to noise. distortion and style variation. and gives high accuracy results when used for training and testing in a statistical or neural classifier. A very powerful classifier is therefore obtained for recognizing correctly segmented characters. The segmentation task is explored in a simple system of integrated over-segmentation. Character classification and approximate dictionary checking. This can be extended to a full system for handprinted word recognition. In addition to the advancements made by these methods. a powerful new approach to handwritten character recognition is proposed as a direction for future research. This proposal combines the ideas and techniques developed in this thesis in a hierarchical network of classifier modules to achieve context-sensitive. off-line recognition of handwritten text. A new type of "intelligent" feedback is used to direct the search to contextually sensible classifications. A powerful adaptive segmentation system is proposed which. when used as the bottom layer in the hierarchical network. allows initially incorrect segmentations to be adjusted according to the hypotheses of the higher level context modules