Malayalam Handwritten Character Recognition Using AlexNet Based Architecture

Ajay James, Manjusha J, Chandran Saravanan


This research article proposes a new handwritten Malayalam character recognition model based on AlexNet based architecture. The Malayalam language consists of a variety of characters having similar features, thus, differentiating characters is a challenging task. A lot of handcrafted feature extraction methods have been used for the classification of Malayalam characters. Convolutional Neural Networks (CNN) is one of the popular methods used in image and language recognition. AlexNet based CNN is proposed for feature extraction of basic and compound Malayalam characters. Furthermore, Support Vector Machine (SVM) is used for classification of the Malayalam characters. The 44 primary and 36 compound Malayalam characters are recognised with better accuracy and achieved minimal time consumption using this model. A dataset consisting of about 180,000 characters is used for training and testing purposes. This proposed model produces an efficiency of 98% with the dataset. Further, a dataset for Malayalam characters is developed in this research work and shared on Internet


Keywords: Optical Character Recognition, Convolutional Neural Network, AlexNet, Handwritten character recognition, Malayalam character recognition

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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

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

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