Abstract
Optical Character Recognition is one of the rapidly growing areas of Artificial Intelligence due to its vast applicability. The technique is used to recognize characters printed on paper or elsewhere. The optical character recognition gains more importance when there are multiple languages present. The complexity of the problem increases for the addition of every language. The identification of character is both difficult and important in the presence of multiple languages. In this paper we propose a technique for multi lingual character identification. The algorithm uses the characteristics of the language to find out the language first. This is done using a rule-based approach. Then we apply neural network of the particular language to find out the exact character. We have coded and tested this using English capital letters, English small letters and Hindi letters. We got an appreciable accuracy using test cases of all languages. This proves the efficiency of the algorithm.
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Shukla, A., Tiwari, R., Ranjan, A., Kala, R. (2009). Multi Lingual Character Recognition Using Hierarchical Rule Based Classification and Artificial Neural Network. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_92
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DOI: https://doi.org/10.1007/978-3-642-01510-6_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01509-0
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