Tamil Handwritten Character Recognition System using Statistical Algorithmic Approaches. (March 2023)

• Record Type: Journal Article
• Authors: Antony Robert Raj, M.; Abirami, S.; Shyni, S.M.
• Is Part Of: Computer speech & language. Volume 78(2023)
• Publication Date: 2023-03
• Subjects: Tamil handwritten character recognition -- Quad divisions -- Directional and locational Features -- Support Vector Machine -- Shape prediction; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2022.101448
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: This framework gives a detailed research on recognizing Tamil handwritten characters using locational and directional approaches embedded with different combinations of zone and quad methodologies. Tamil language has 247 character classes and is widely spoken by the people in India (Tamil Nadu), Malaysia, Singapore, Sri Lanka and so on. For considering the large character sets with their general and handwritten complexities, the two-stage feature extraction process has been experimented with to represent the character's structure. In the initial stage, the character's image is divided into nine equal zones and the structural features were extracted from each zone by the directional algorithmic approach, which denotes unique shape possibilities represented in zone divisions. A classification test has been performed to identify characters in this stage, but a structural portion of handwritten characters like unwanted loops and curves leads to negative results. Hence, locational features have been introduced to identify the position of structures. Each zone is subdivided into four quads further and the pixel availability has been taken as features from the quads to provide the solution for unnecessary portions and loops. With directional features taken from upper (3 columns × 1 row) and lower zones (3 columns × 1 row), corresponding location features have been added up for labeling a unique shape. Finally, to classify the characters, the directional features taken from middle zones (3 columns × 1 row) and their respective locational features have been added with labeled shapes of upper and lower zones. A suitable machine learning algorithm has been chosen for classifying the character classes. HP-Lab-India dataset and two different handwritten documents collected from the people of Tamil Nadu, India, have been tested by these approaches. This experimental research shows significant improvement in recognizing accurate characters. The final results of this approach have created a benchmark for the recognition of handwritten Tamil characters.