Handwritten digit recognition: benchmarking of state-of-the-art techniques

Online handwriting recognition is gaining renewed interest owing to the increase of pen computing applications and new pen input devices. The recognition of Chinese characters is different from western handwriting recognition and poses a special challenge. To provide an overview of the technical status and inspire future research, this paper reviews the advances in online Chinese character recognition (OLCCR), with emphasis on the research works from the 1990s. Compared to the research in the 1980s, the research efforts in the 1990s aimed to further relax the constraints of handwriting, namely, the adherence to standard stroke orders and stroke numbers and the restriction of recognition to isolated characters only. The target of recognition has shifted from regular script to fluent script in order to better meet the requirements of practical applications. The research works are reviewed in terms of pattern representation, character classification, learning/adaptation, and contextual processing. We compare important results and discuss possible directions of future research.

'Online recognition of Chinese characters: the state-of-the-art

Figures. List of Tables. Preface. Acknowledgments. Acronyms. 1. Introduction: Character Recognition, Evolution and Development. 1.1 Generation and Recognition of Characters. 1.2 History of OCR. 1.3 Development of New Techniques. 1.4 Recent Trends and Movements. 1.5 Organization of the Remaining Chapters. References. 2. Tools for Image Pre-Processing. 2.1 Generic Form Processing System. 2.2 A Stroke Model for Complex Background Elimination. 2.2.1 Global Gray Level Thresholding. 2.2.2 Local Gray Level Thresholding. 2.2.3 Local Feature Thresholding-Stroke Based Model. 2.2.4 Choosing the Most Efficient Character Extraction Method. 2.2.5 Cleaning up Form Items Using Stroke Based Model. 2.3 A Scale-Space Approach for Visual Data Extraction. 2.3.1 Image Regularization. 2.3.2 Data Extraction. 2.3.3 Concluding Remarks. 2.4 Data Pre-Processing. 2.4.1 Smoothing and Noise Removal. 2.4.2 Skew Detection and Correction. 2.4.3 Slant Correction. 2.4.4 Character Normalization. 2.4.5 Contour Tracing/Analysis. 2.4.6 Thinning. 2.5 Chapter Summary. References 72. 3. Feature Extraction, Selection and Creation. 3.1 Feature Extraction. 3.1.1 Moments. 3.1.2 Histogram. 3.1.3 Direction Features. 3.1.4 Image Registration. 3.1.5 Hough Transform. 3.1.6 Line-Based Representation. 3.1.7 Fourier Descriptors. 3.1.8 Shape Approximation. 3.1.9 Topological Features. 3.1.10 Linear Transforms. 3.1.11 Kernels. 3.2 Feature Selection for Pattern Classification. 3.2.1 Review of Feature Selection Methods. 3.3 Feature Creation for Pattern Classification. 3.3.1 Categories of Feature Creation. 3.3.2 Review of Feature Creation Methods. 3.3.3 Future Trends. 3.4 Chapter Summary. References. 4. Pattern Classification Methods. 4.1 Overview of Classification Methods. 4.2 Statistical Methods. 4.2.1 Bayes Decision Theory. 4.2.2 Parametric Methods. 4.2.3 Non-ParametricMethods. 4.3 Artificial Neural Networks. 4.3.1 Single-Layer Neural Network. 4.3.2 Multilayer Perceptron. 4.3.3 Radial Basis Function Network. 4.3.4 Polynomial Network. 4.3.5 Unsupervised Learning. 4.3.6 Learning Vector Quantization. 4.4 Support Vector Machines. 4.4.1 Maximal Margin Classifier. 4.4.2 Soft Margin and Kernels. 4.4.3 Implementation Issues. 4.5 Structural Pattern Recognition. 4.5.1 Attributed String Matching. 4.5.2 Attributed Graph Matching. 4.6 Combining Multiple Classifiers. 4.6.1 Problem Formulation. 4.6.2 Combining Discrete Outputs. 4.6.3 Combining Continuous Outputs. 4.6.4 Dynamic Classifier Selection. 4.6.5 Ensemble Generation. 4.7 A Concrete Example. 4.8 Chapter Summary. References. 5. Word and String Recognition. 5.1 Introduction. 5.2 Character Segmentation. 5.2.1 Overview of Dissection Techniques. 5.2.2 Segmentation of Handwritten Digits. 5.3 Classification-Based String Recognition. 5.3.1 String Classification Model. 5.3.2 Classifier Design for String Recognition. 5.3.3 Search Strategies. 5.3.4 Strategies for Large Vocabulary. 5.4 HMM-Based Recognition. 5.4.1 Introduction to HMMs. 5.4.2 Theory and Implementation. 5.4.3 Application of HMMs to Text Recognition. 5.4.4 Implementation Issues. 5.4.5 Techniques for Improving HMMs' Performance. 5.4.6 Summary to HMM-Based Recognition. 5.5 Holistic Methods For Handwritten Word Recognition. 5.5.1 Introduction to Holistic Methods. 5.5.2 Overview of Holistic Methods. 5.5.3 Summary to Holistic Methods. 5.6 Chapter Summary. References. 6. Case Studies. 6.1 Automatically Generating Pattern Recognizers with Evolutionary Computation. 6.1.1 Motivation. 6.1.2 Introduction. 6.1.3 Hunters and Prey. 6.1.4 Genetic Algorithm. 6.1.5 Experiments. 6.1.6 Analysis. 6.1.7 Future Directions. 6.2 Offline Handwritten Chinese Character Recognition. 6.2.1 Related Works. 6.2.2 System Overview. 6.2.3 Character Normalization. 6.2.4 Direction Feature Extraction. 6.2.5 Classification Methods. 6.2.6 Experiments. 6.2.7 Concluding Remarks. 6.3 Segmentation and Recognition of Handwritten Dates on Canadian Bank Cheques. 6.3.1 Introduction. 6.3.2 System Architecture. 6.3.3 Date Image Segmentation. 6.3.4 Date Image Recognition. 6.3.5 Experimental Results. 6.3.6 Concluding Remarks. References.

Character Recognition Systems: A Guide for Students and Practitioners

Handwritten digit recognition: investigation of normalization and feature extraction techniques

This paper presents a study of using 8-directional features for online handwritten Chinese character recognition. Given an online handwritten character sample, a series of processing steps, including linear size normalization, adding imaginary strokes, nonlinear shape normalization, equidistance resampling, and smoothing, are performed to derive a 64/spl times/64 normalized online character sample. Then, 8-directional features are extracted from each online trajectory point, and 8 directional pattern images are generated accordingly, from which blurred directional features are extracted at 8/spl times/8 uniformly sampled locations using a filter derived from the Gaussian envelope of a Gabor filter. Finally, a 512-dimensional vector of raw features is formed. Extensive experiments on the task of recognizing 3755 level-1 Chinese characters in GB2312-80 standard are performed to compare and discern the best setting for several algorithmic choices and control parameters. The effectiveness of the studied approach is confirmed.

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A study on the use of 8-directional features for online handwritten Chinese character recognition

The authors propose an online handwritten Japanese character recognition method permitting both stroke number and stroke order variations. The method is based on the pattern matching technique. Matching is done by the multiple similarity method using directional feature densities, which are independent of both stroke number and stroke order. This method has achieved a good recognition rate, 91%, for 2965 freely written Japanese kanji characters. >

K. Yura

Papers

Online recognition of freely handwritten Japanese characters using directional feature densities