Ancient Tamil Character Recognition Based on Edge Mapping Pointed Multi Perspective Neural Network for Enhanced Font Definition

Authors

  • P. Selvakumar

Keywords:

Text Recognition, Feature selection and classification, neural network, edge mapping, stroke transformation, segmentation, structure projection

Abstract

Ancient text Detection and extraction strategy for text assumes an indispensable part in numerous computation model using recognition of content detection in images. Base up techniques don't generally detect the specified region of text area. To resolve this problem, we propose an Edge Mapping Pointed Multi Perspective Neural Network (EMP-MPNN) for enhanced font definition. Initially the preprocessing make scaling to noise removal from ancient character dataset. TheCanny Morphed Edge Bounding Text Region(CMEBTR) is applied to find the character edges accuracy by cornering using Stroke patch text extraction. This increase the object entity relation of pixel coordination of character lining to identifying the text regions. Further the Scaled Inline Skeletonized Segmentation (SISS) are applied to select the inline features of the text to find high attention of the text structure.The strategies specifically images are split into segments and after that gathering character region covers the text into dependable regions by maximum match case weight.Then features extracted through Wavelet Transformed Featured Extraction(WTFE) and trained into Multi Perspective Neural Network (MPNN) to identify the classes. The proposed multi objective feature selection implementation approach which reduces the error rate with precision recall rate have higher performance.

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Published

26.03.2024

How to Cite

Selvakumar, P. . (2024). Ancient Tamil Character Recognition Based on Edge Mapping Pointed Multi Perspective Neural Network for Enhanced Font Definition. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 1786–1794. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5588

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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