Publication Type:

Journal Article

Source:

Advances in Intelligent Systems and Computing, Volume 328, Bhubaneswar; India, p.493-500 (2015)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919359412&partnerID=40&md5=10ad0014a142c4e8e5ea95ae2da50972

Abstract:

Optical Character Recognition (OCR) has an important role in information retrieval which converts scanned documents into machine editable and searchable text formats. This work is focussing on the recognition part of OCR. LeNet-5, a Convolutional Neural Network (CNN) trained with gradient based learning and backpropagation algorithm is used for classification of Malayalam character images. Result obtained for multi-class classifier shows that CNN performance is dropping down when the number of classes exceeds range of 40. Accuracy is improved by grouping misclassified characters together. Without grouping, CNN is giving an average accuracy of 75% and after grouping the performance is improved upto 92%. Inner level classification is done using multi-class SVM which is giving an average accuracy in the range of 99-100%. © Springer International Publishing Switzerland 2015.

Cite this Research Publication

R. Anil, Manjusha, K., S. Kumar, S., and Soman, K. P., “Convolutional neural networks for the recognition of malayalam characters”, Advances in Intelligent Systems and Computing, vol. 328, pp. 493-500, 2015.

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