Method of combining the classification powers of several classifiers is regarded as a general problem in various application areas of pattern recognition, and a systematic investigation has been made. Possible solutions to the problem can be divided into three categories according to the levels of information available from the various classifiers. Four approaches are proposed based on different methodologies for solving this problem. One is suitable for combining individual classifiers such as Bayesian, k-NN and various distance classifiers. The other three could be used for combining any kind of individual classifiers. On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers could be improved significantly. For example, on the U.S. zipcode database, the result of 98.9% recognition with 0.90% substitution and 0.2% rejection can be obtained, as well as a high reliability with 95% recognition, 0% substitution and 5% rejection. These results compared favorably to other research p u p s in Europe, Asia, and North America.

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This article is a comprehensive survey of thinning methodologies. It discusses the wide range of thinning algorithms, including iterative deletion of pixels and nonpixel-based methods, whereas skeletonization algorithms based on medial axis and other distance transforms will be the subject matter of a subsequent study. This self-contained paper begins with an overview of the iterative thinning process and the pixel-deletion criteria needed to preserve the connectivity of the image pattern. Thinning algorithms are then considered in terms of these criteria as well as their modes of operation. This is followed by a discussion of nonpixel-based methods that usually produce a center line of the pattern directly in one pass without examining all the individual pixels. Algorithms are considered in greater detail and scope here than in other surveys, and the relationships among them are also explored.

This survey describes the state of the art of on-line handwriting recognition during a period of renewed activity in the field. It is based on an extensive review of the literature, including journal articles, conference proceedings, and patents. Shape recognition algorithms, preprocessing and postprocessing techniques, experimental systems, and commercial products are examined. Index Terms-Natural input to computers, on-line handwriting recognition, real-time character recognition, tablet digitizers.

In this paper research and development of OCR systems are considered from U historical point of t k v . The paper is mainly divided into two parts: the research and development of OCR systems, and the historical development of commercial OCR's. The R&D part is further diLided into two approaches: template matching and structure analysis. It has been shown that both approaches are coming closer and closer to each other arid it seems they tend to merge into one big stream. On [he other hand, commercial products can be classified into three generations, for each of which some represenfative OCR systems are chosen and described in some detail. Some comments on recent techniques applied to OCR such as expert systems, neural networks, and some open problems are also raised in this paper. Finally we present our views and hopes on future trends in this fascinating area.

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This article focusses on the problems of feature extraction and the recognition of handwritten digits. A trainable feature extractor based on the LeNet5 convolutional neural network architecture is introduced to solve the first problem in a black box scheme without prior knowledge on the data. The classification task is performed by Support Vector Machines to enhance the generalization ability of LeNet5. In order to increase the recognition rate, new training samples are generated by affine transformations and elastic distortions. Experiments are performed on the well known MNIST database to validate the method and the results show that the system can outperfom both SVMs and LeNet5 while providing performances comparable to the best performance on this database. Moreover, an analysis of the errors is conducted to discuss possible means of enhancement and their limitations.