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

Junction Point Detection And Identification Of Broken Character In Touching Arabic Handwritten Text Using Overlapping Set Theory

Touching characters are formed when two or more characters share the same space with each other. Therefore, segmentation of these touching character is very challenging research topic especially for handwritten Arabic degraded documents. This is one of the key issue in recognition of the handwritten Arabic text. In order to make the recognition system more effective segmentation of these touching handwritten Arabic characters is considered to be very important research area. In this research, a new method is proposed, which is used to identify the junction or common point of Arabic touching word image by applying overlapping or intersection set theory operation, which will help to trace the correct boundary of the touching characters, identify the broken characters and also segmented these touching handwritten text in an efficient way. The proposed method has been evaluated on Arabic touching handwritten characters taken from handwritten datasets. The results show the efficiency of the proposed method. The proposed method is applicable to both degraded handwritten documents and printed documents

Feature Extraction Methods for Character Recognition

Not Include

AutoGraff: towards a computational understanding of graffiti writing and related art forms.

The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes

Content Recognition and Context Modeling for Document Analysis and Retrieval

The nature and scope of available documents are changing significantly in many areas of document analysis and retrieval as complex, heterogeneous collections become accessible to virtually everyone via the web. The increasing level of diversity presents a great challenge for document image content categorization, indexing, and retrieval. Meanwhile, the processing of documents with unconstrained layouts and complex formatting often requires effective leveraging of broad contextual knowledge. In this dissertation, we first present a novel approach for document image content categorization, using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant local shape feature that is generic enough to be detected repeatably and is segmentation free. A concise, structurally indexed shape lexicon is learned by clustering and partitioning feature types through graph cuts. Our idea finds successful application in several challenging tasks, including content recognition of diverse web images and language identification on documents composed of mixed machine printed text and handwriting. Second, we address two fundamental problems in signature-based document image retrieval. Facing continually increasing volumes of documents, detecting and recognizing unique, evidentiary visual entities (\eg, signatures and logos) provides a practical and reliable supplement to the OCR recognition of printed text. We propose a novel multi-scale framework to detect and segment signatures jointly from document images, based on the structural saliency under a signature production model. We formulate the problem of signature retrieval in the unconstrained setting of geometry-invariant deformable shape matching and demonstrate state-of-the-art performance in signature matching and verification. Third, we present a model-based approach for extracting relevant named entities from unstructured documents. In a wide range of applications that require structured information from diverse, unstructured document images, processing OCR text does not give satisfactory results due to the absence of linguistic context. Our approach enables learning of inference rules collectively based on contextual information from both page layout and text features. Finally, we demonstrate the importance of mining general web user behavior data for improving document ranking and other web search experience. The context of web user activities reveals their preferences and intents, and we emphasize the analysis of individual user sessions for creating aggregate models. We introduce a novel algorithm for estimating web page and web site importance, and discuss its theoretical foundation based on an intentional surfer model. We demonstrate that our approach significantly improves large-scale document retrieval performance

An Analysis of The Handwriting of Elderly Chinese Subjects.

Nowadays, one of the issues of concern that brought on by the aging population is legal disputes that have been increasing in number every year. One core problem in these legal disputes involves handwriting identification. There are however not many previous studies in China that have been concentrated on this field. Hence, the study on elderly Chinese subjects\u27 handwriting has an extremely broad potential value for its application for academic usage and practical meaning. This study uses an empirical technique, gathering first hand data and analysing them by employing proper methods. There are 7 handwriting features and characteristics that have been found in the study to represent the key characteristics of the elderly Chinese subjects\u27 handwriting. They are: Pen pressure, occurrence rate of traditional Chinese words, letter size, poor structure, tremor, half-way stop sign, and towing sign at beginning and end of strokes

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

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

Recognition of off-line handwritten cursive text

The author presents novel algorithms to design unconstrained handwriting recognition systems organized in three parts: In Part One, novel algorithms are presented for processing of Arabic text prior to recognition. Algorithms are described to convert a thinned image of a stroke to a straight line approximation. Novel heuristic algorithms and novel theorems are presented to determine start and end vertices of an off-line image of a stroke. A straight line approximation of an off-line stroke is converted to a one-dimensional representation by a novel algorithm which aims to recover the original sequence of writing. The resulting ordering of the stroke segments is a suitable preprocessed representation for subsequent handwriting recognition algorithms as it helps to segment the stroke. The algorithm was tested against one data set of isolated handwritten characters and another data set of cursive handwriting, each provided by 20 subjects, and has been 91.9% and 91.8% successful for these two data sets, respectively. In Part Two, an entirely novel fuzzy set-sequential machine character recognition system is presented. Fuzzy sequential machines are defined to work as recognizers of handwritten strokes. An algorithm to obtain a deterministic fuzzy sequential machine from a stroke representation, that is capable of recognizing that stroke and its variants, is presented. An algorithm is developed to merge two fuzzy machines into one machine. The learning algorithm is a combination of many described algorithms. The system was tested against isolated handwritten characters provided by 20 subjects resulting in 95.8% recognition rate which is encouraging and shows that the system is highly flexible in dealing with shape and size variations. In Part Three, also an entirely novel text recognition system, capable of recognizing off-line handwritten Arabic cursive text having a high variability is presented. This system is an extension of the above recognition system. Tokens are extracted from a onedimensional representation of a stroke. Fuzzy sequential machines are defined to work as recognizers of tokens. It is shown how to obtain a deterministic fuzzy sequential machine from a token representation that is capable'of recognizing that token and its variants. An algorithm for token learning is presented. The tokens of a stroke are re-combined to meaningful strings of tokens. Algorithms to recognize and learn token strings are described. The. recognition stage uses algorithms of the learning stage. The process of extracting the best set of basic shapes which represent the best set of token strings that constitute an unknown stroke is described. A method is developed to extract lines from pages of handwritten text, arrange main strokes of extracted lines in the same order as they were written, and present secondary strokes to main strokes. Presented secondary strokes are combined with basic shapes to obtain the final characters by formulating and solving assignment problems for this purpose. Some secondary strokes which remain unassigned are individually manipulated. The system was tested against the handwritings of 20 subjects yielding overall subword and character recognition rates of 55.4% and 51.1%, respectively

Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p