Next-Generation Handwriting Recognition Powered By AI

Abstract

Character segmentation and recognition are essential tasks in image processing and computer vision, with various applications in text recognition, document analysis, and optical character recognition (OCR) systems. The procedure entails isolating individual characters from an input image, succeeded by the identification of these segmented characters. This study provides a thorough examination of character segmentation and recognition techniques, examining both conventional methods and contemporary innovations. Character segmentation approaches are classified into two primary categories: linked component-based methods and contour-based methods. Connected component-based techniques depend on recognizing distinct characters through connected regions in the image, whereas contour-based techniques emphasize character segmentation via edge detection and contour analysis. Furthermore, we investigate diverse methodologies for character identification, encompassing template matching, feature-based approaches, and deep learning techniques. Template matching entails the comparison of segmented characters against established templates to ascertain correspondences, whereas feature-based approaches derive pertinent properties from characters and utilize classifiers for recognition. Deep learning approaches have garnered considerable interest for their capacity to autonomously acquire discriminative features from unprocessed data, attaining superior performance in character recognition challenges. Furthermore, we examine the obstacles and prospective avenues in character segmentation and recognition, including managing intricate backdrops, addressing diverse fonts and writing styles, and enhancing performance on degraded or handwritten text. We emphasize the significance of dataset diversity and robustness in creating precise and adaptable segmentation and identification algorithms.

DOI:  https://doi-ds.org/doilink/12.2025-57716868