Extraction of Tamil Characters from a Handwritten Document using Connected Component Labeling - Rajalakshmi, D; Jayanthi, SK (International Journal of Computer Sciences and Engineering, 2025)
Writer identification is a challenging task for the reason that it requires textural features and structural features. Textural features like grey-level co-occurrence matrices, Gabor filters can be extracted from entire page or a block of text. The structural features like slant and skew, character height, stroke width, frequency of loops or blobs etc. also characterize the handwriting style. Before extracting character level features it is a prerequisite to segment the document image into characters. This paper proposes a connected component oriented approach to segment an image of handwritten Tamil document into individual characters. The features extracted from these characters then can be used for writer identification.