An efficient convolutional neural network based classifier to predict Tamil writer

Thendral Tharmalingam, Vijaya Vijayakumar


Identification of Tamil handwritten calligraphies at different levels such as character, word and paragraph is complicated when compared to other western language scripts. None of the existing methods provides efficient Tamil handwriting writer identification (THWI). Also offline Tamil handwritten identification at different levels still offers many motivating challenges to researchers. This paper employs a deep learning algorithm for handwriting image classification. Deep learning has its own dimensions to generate new features from a limited set of training dataset. Convolutional Neural Networks (CNNs) is one of deep, feed-forward artificial neural network is applied to THWI. The dataset collection and classification phase of CNN enables data access and automatic feature generation. Since the number of parameters is significantly reduced, training time to THWI is proportionally reduced. Understandably, the CNNs produced much higher identification rate compared with traditional ANN at different levels of handwriting.

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ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License