Script-Based Handwritten Document Classification through Texture Descriptors

Author(s)	Veershetty
Country	India
Abstract	This study presents an innovative approach for page-level script identification in Indian handwritten documents using a combination of Histogram of Oriented Gradients (HOG) and Uniform Local Phase Binary (ULPB) features, coupled with three well-established classifiers: Linear Support Vector Machine (LSVM), k-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA). Through rigorous experimentation and evaluation, the proposed method demonstrates superior accuracy and robustness in discerning between diverse script types encountered in Indian documents. This advancement holds significant potential in automating document analysis and retrieval processes, thereby contributing to the preservation and exploration of India's rich cultural and linguistic heritage
Keywords	Word spotting, OCR,KNN,Multilingual,LDA
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 4, Issue 4, July-August 2022
Published On	2022-08-23
Cite This	Script-Based Handwritten Document Classification through Texture Descriptors - Veershetty - IJFMR Volume 4, Issue 4, July-August 2022.

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International Journal For Multidisciplinary Research