Publication Type : Journal Article
Thematic Areas : Center for Computational Engineering and Networking (CEN)
Publisher : Springer
Source : International Conference on Frontiers of Intelligent Computing, Springer, Vol 328,pp 493-500, 2014 (Scopus)
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Center for Computational Engineering and Networking (CEN), Electronics and Communication
Verified : Yes
Year : 2014
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, Sachin Kumar S, KP Soman, Convolutional Neural Network for the Recognition of Malayalam Characters, International Conference on Frontiers of Intelligent Computing, Springer, Vol 328,pp 493-500, 2014 (Scopus)