Abstract
Off-line Handwritten Character Recognition (HCR) is the process of automatic conversion of images of handwritten text into a form that computers can understand and process. Several research works for HCR of different scripts are found in literature. They make use of one or more feature sets and classification tools for recognition of characters. Recently, Convolutional Neural Network (CNN) based recognition is found to show significantly better results. However, only a handful of studies are found of Meitei Mayek script and none based on CNN. Also, no dataset is available publicly for the said script. In order to study the recognition of characters for a particular script, a significantly large dataset is needed. In this paper, for the first time, a dataset consisting of 60285 handwritten characters of Meitei Mayek script is introduced which will be made publicly available to the researchers for use at http://agnigarh.tezu.ernet.in/~sarat/resources.html. A CNN architecture is also proposed for the recognition of characters in the dataset. An accuracy of 96.24% is achieved which is promising as compared to state-of-the-art works for the concerned script.
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Hijam, D., Saharia, S. (2018). Convolutional Neural Network Based Meitei Mayek Handwritten Character Recognition. In: Tiwary, U. (eds) Intelligent Human Computer Interaction. IHCI 2018. Lecture Notes in Computer Science(), vol 11278. Springer, Cham. https://doi.org/10.1007/978-3-030-04021-5_19
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