Abstract
In this paper, we exploit the texture feature of local binary pattern (LBP) for handwritten Odia numeral recognition. There are several challenges in the handwritten recognition due to the different writing style of the individuals. The histogram of the block-based LBP is computed considering the block sizes of (4 × 4), (8 × 8) and the whole image and these features are used to classify the numerals. For classification, SVM and single decision tree classifiers have been employed. In this experiment, we evaluated the performance of the block-based LBP on the ISI, Kolkata Odia numeral digital database. The performance of (4 × 4) block-based LBP shows high level of accuracy (97%) for both SVM and single tree classifiers. Single decision tree classifier shows promising results in terms of computational time and hence can be used for real-time applications.
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Behera, S., Das, N. (2021). Block-Based Local Binary Pattern for Recognition of Handwritten Odia Numerals. In: Sabut, S.K., Ray, A.K., Pati, B., Acharya, U.R. (eds) Proceedings of International Conference on Communication, Circuits, and Systems. Lecture Notes in Electrical Engineering, vol 728. Springer, Singapore. https://doi.org/10.1007/978-981-33-4866-0_10
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DOI: https://doi.org/10.1007/978-981-33-4866-0_10
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