Year: 2024
Type: conference-paper
Abstract: Kurdish handwritten character recognition (KHCR) is an emerging field, which deals with the complications brought forth in recognizing Kurdish handwritten text due to the peculiar characteristics of various scripts and dialects of Kurdish. A review on the status of KHCR was discussed here; important challenges in recognition were listed as data scarcity, intricacies in cursive writing, and variability in the individual's handwriting style. The existing works within this area have been involved in the development of neural network models and datasets; however, there are still a lot of limitations concerning the completeness of available datasets and the precision of the recognition system. The future directions in KHCR research put weight on developing largescale and diverse datasets, including all forms of Kurdish letters, using advanced techniques in deep learning, and realizing realtime recognition systems. This research has come up with a hybrid model combining the traditional approaches of feature extraction with the modern methods of machine learning, including user adaptation in order to improve the accuracy, and dialectal variability of Kurdish handwriting-all these will be very important in the future. By focusing on these areas, KHCR can significantly enhance digital communication and accessibility for Kurdish speakers while facilitating the preservation and digitalization of the Kurdish language. This continuous research in the domain carries the potential to create an inclusive digital environment that will also enable further expansion in use and application related to Kurdish handwriting recognition technologies.
Authors: Islam A. Qader, Tarik A. Rashid
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412130455