Feature Extraction Methods for Character Recognition

Not Include

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

The effectiveness of features in pattern recognition

Imperial Users onl

Metaheuristic approach on feature extraction and classification algorithm for handwrittten character recognition

Handwritten Character Recognition (HCR) is a process of converting handwritten text into machine readable form and it comprises three stages; preprocessing, feature extraction and classification. This study acknowledged the issues regarding HCR performances particularly at the feature extraction and classification stages. In relation to feature extraction stage, the problem identified is related to continuous and minimum chain code feature extraction at its starting and revisit points due to branches of handwritten character. As for the classification stage, the problems identified are related to the input feature for classification that results in low accuracy of classification and classification model particularly in Artificial Neural Network (ANN) learning problem. Thus, the aim of this study is to extract the continuous chain code feature for handwritten character along with minimising its length and then proceed to develop and enhance the ANN classification model based on the extracted chain code in order to identify the handwritten character better. Four phases were involved in accomplishing the aim of this study. First, thinning algorithm was applied to remove the redundancies of pixel in handwritten character binary image. Second, graph based-metaheuristic feature extraction algorithm was proposed to extract the continuous chain code feature of the handwritten character image while minimising the route length of the chain code. Graph theory was then utilised as a solution representation. Hence, two metaheuristic approaches were adopted; Harmony Search Algorithm (HSA) and Flower Pollination Algorithm (FPA). As a result, HSA graphbased metaheuristic feature extraction algorithm was proposed to extract the continuous chain code feature for handwritten character. Based on the experiment conducted, it was demonstrated that the HSA graph-based metaheuristic feature extraction algorithm showed better performance in generating the shortest route length of chain code with minimum computational time compared to FPA. Furthermore, based on the evaluation of previous works, the proposed algorithm showed notable performance in terms of shortest route length of chain code for extracting handwritten character. Third, a feature vector was derived to address the input feature issue. The derivation of feature vector based on proposed formation rule namely Local Value Formation Rule (LVFR) and Global Value Formation Rule (GVFR) was adopted to create the image features for classification purpose. ANN was applied to classify the handwritten character based on the derived feature vector. Fourth, a hybrid of Firefly Algorithm (FA) and ANN (FA-ANN) classification model was proposed to solve the ANN network learning issue. Confusion Matrix was generated to evaluate the performance of the model in terms of precision, sensitivity, specificity, F-score, accuracy and error rate. As a result, the proposed hybrid FA-ANN classification model is superior in classifying the handwritten characters compared to the proposed feature vector-based ANN with 1.59 percent incremental in terms of accuracy model. Furthermore, the proposed hybrid FA-ANN also exhibits better performances compared to previous related works on HCR

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