Feature extraction methods for character recognition-A survey

-This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representations 6f the characters, such as solid binary characters, character contours, skeletons (thinned characters) or gray-level subimages of each individual character. The feature extraction methods are discussed in terms of invariance properties, reconstructability and expected distortions and variability of the characters. The problem of choosing the appropriate feature extraction method for a given application is also discussed. When a few promising feature extraction methods have been identified, they need to be evaluated experimentally to find the best method for the given application. Feature extraction Optical character recognition Character representation Invariance Reconstructability I. I N T R O D U C T I O N Optical character recognition (OCR) is one of the most successful applications of automatic pattern recognition. Since the mid 1950s, OCR has been a very active field for research and development, ca) Today, reasonably good OCR packages can be bought for as little as $100. However, these are only able to recognize high quality printed text documents or neatly written handprinted text. The current research in OCR is now addressing documents that are not well handled by the available systems, including severely degraded, omnifont machine-printed text and (unconstrained) handwritten text. Also, efforts are being made to achieve lower substitution error rates and reject rates even on good quality machine-printed text, since an experienced human typist still has a much lower error rate, albeit at a slower speed. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance. Our own interest in character recognition is to recognize hand-printed digits in hydrographic maps , but we have tried not to emphasize this particular application in the paper. Given the large number of feature extraction methods reported in the literature, a newcomer to the field is faced with the following question: which feature ext Author to whom correspondence should be addressed. This work was done while OD. Trier was visiting Michigan State University. traction method is the best for a given application? This question led us to characterize the available feature extraction methods, so that the most promising methods could be sorted out. An experimental evaluation of these few promising methods must still be performed to select the best method for a specific application. In this process, one might find that a specific feature extraction method needs to be further developed. A full performance evaluation of each method in terms of classification accuracy and speed is not within the scope of this review paper. In order to study performance issues, we will have to implement all the feature extraction methods, which is an enormous task. In addition, the performance also depends on the type of classifier used. Different feature types may need different types of classifiers. Also, the classification results reported in the literature are not comparable because they are based on different data sets. Given the vast number of papers published on OCR every year, it is impossible to include all the available feature extraction methods in this survey. Instead, we have tried to make a representative selection to illustrate the different principles that can be used. Two-dimensional (2-D) object classification has several applications in addition to character recognition. These include airplane recognition, 12) recognition of mechanical parts and tools, 13l and tissue classification in medical imaging34) Several of the feature extraction techniques described in this paper for OCR have also been found to be useful in such applications.