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Traditionally, the performance of ocr algorithms and systems is based on therecognition of isolated characters. When a system classifies an individualcharacter, its output is typically a character label or a reject marker thatcorresponds to an unrecognized character. By comparing output labels with thecorrect labels, the number of correct recognition, substitution errorsmisrecognized characters, and rejects unrecognized characters are determined.Nowadays, although recognition of printed isolated characters is performed withhigh accuracy, recognition of handwritten characters still remains an openproblem in the research arena. The ability to identify machine printedcharacters in an automated or a semi automated manner has obvious applicationsin numerous fields. Since creating an algorithm with a one hundred percentcorrect recognition rate is quite probably impossible in our world of noise anddifferent font styles, it is important to design character recognitionalgorithms with these failures in mind so that when mistakes are inevitablymade, they will at least be understandable and predictable to the personworking with the

An Efficient Hidden Markov Model for Offline Handwritten Numeral Recognition