Character recognition (CR) has been extensively studied in the last half century and progressed to a level sufficient to produce technology driven applications. Now, the rapidly growing computational power enables the implementation of the present CR methodologies and creates an increasing demand on many emerging application domains, which require more advanced methodologies.This material serves as a guide and update for readers working in the CR area. First, the historical evolution of CR systems is presented. Then, the available CR techniques with their superiorities and weaknesses are reviewed. Finally, the current status of CR is discussed, and directions for future research are suggested. Special attention is given to the off-line handwriting recognition since this area requires more research to reach the ultimate goal of machine simulation of human reading.
AbstractÐIn this paper, a new analytic scheme, which uses a sequence of segmentation and recognition algorithms, is proposed for offline cursive handwriting recognition problem. First, some global parameters, such as slant angle, baselines, and stroke width and height are estimated. Second, a segmentation method finds character segmentation paths by combining gray scale and binary information. Third, Hidden Markov Model (HMM) is employed for shape recognition to label and rank the character candidates. For this purpose, a string of codes is extracted from each segment to represent the character candidates. The estimation of feature space parameters is embedded in HMM training stage together with the estimation of the HMM model parameters. Finally, the lexicon information and HMM ranks are combined in a graph optimization problem for word-level recognition. This method corrects most of the errors produced by segmentation and HMM ranking stages by maximizing an information measure in an efficient graph search algorithm. The experiments in dicate higher recognition rates compared to the available methods reported in the literature.
In this paper, a generic texture descriptor, namely, Statistical Analysis of Structural Information (SASI) is introduced as a representation of texture. SASI is based on statistics of clique autocorrelation coefficients, calculated over structuring windows. SASI defines a set of clique windows to extract and measure various structural properties of texture by using a spatial multi-resolution method. Experimental results, performed on various image databases, indicate that SASI is more successful then the Gabor Filter descriptors in capturing small granularities and discontinuities such as sharp corners and abrupt changes. Due to the flexibility in designing the clique windows, SASI reaches higher average retrieval rates compared to Gabor Filter descriptors. However, the price of this performance is increased computational complexity.
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