So far, no work is reported in the literature for the straightening of handwritten Telugu languages. In handwritten document images, segmenting text lines is a very challenging task due to various reasons like variability in intra baseline skew and inter line distance between text lines. ![]() The proposed system exhibits the recognition efficiency on our own test dataset with an overall accuracy of 83.55% for handwritten characters. The XML database is used for defining the classes for various character templates and the class representations are provided using a novel class structure designed based on XML tags. ![]() The technique of caching is implemented using main database with a cache database maintaining the frequently used character templates for set of all character templates. This paper proposes a technique for feature extraction and classification of Telugu handwritten script based on customized template matching approach with the support of caching technique for better performance. ![]() The feature extraction and classification of characters from such huge number of classes in south Indian language OCRs remains as a non-trivial problem. Multiple combinations of vowels and consonants along with its modifiers led to generation of huge number of classes with respect to character recognition systems. ![]() Feature extraction and classification processes while developing Optical Character Recognition (OCR) systems involve massive computations and quite expensive especially for South Indian scripts.
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