Enhancing the Recognition of Hand Written Telugu Characters: Natural Language Processing and Machine Learning Approach
- Title
- Enhancing the Recognition of Hand Written Telugu Characters: Natural Language Processing and Machine Learning Approach
- Creator
- Reedy B.G.; Meghana G.; Subhadra T.; Mithun B.N.; Yogish D.
- Description
- Handwritten character recognition has wider application in many areas including heritage documents, education, document digitalization, language processing, and assisting the visually handicapped and other related areas. The paper tries to improve the accuracy and efficiency of recognizing handwritten letters of Telugu language scripts, a difficult task for computers. Telugu is most widely spoken language in southern part of India, it has rich cultural heritage. Using the Natural Language Toolkit (NLTK), this study investigates ways to enhance recognition accuracy by analyzing handwritten content and implementing methods such as feature extraction and classification. The purpose is to use NLTK's capabilities to develop handwritten character recognition. 2024 IEEE.
- Source
- Proceedings of InC4 2024 - 2024 IEEE International Conference on Contemporary Computing and Communications
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Machine Learning; Natural Language processing; Optical Character Recognition (OCR); Telugu
- Coverage
- Reedy B.G., Christ University, Karnataka, Bangalore, India; Meghana G., Christ University, Karnataka, Bangalore, India; Subhadra T., Christ University, Karnataka, Bangalore, India; Mithun B.N., A P S College of Engineering, Dept of CSE, Karnataka, Bangalore, India; Yogish D., Christ University, Dept of CSE, Karnataka, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835038365-2
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Reedy B.G.; Meghana G.; Subhadra T.; Mithun B.N.; Yogish D., “Enhancing the Recognition of Hand Written Telugu Characters: Natural Language Processing and Machine Learning Approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19259.