Ocr system framework for modi scripts using data augmentation and convolutional neural network
- Title
- Ocr system framework for modi scripts using data augmentation and convolutional neural network
- Creator
- Joseph S.; Datta A.; Anto O.; Philip S.; George J.
- Description
- Character recognition is one of the most active research areas in the field of pattern recognition and machine intelligence. It is a technique of recognizing either printed or handwritten text from document images and converting it to a machine-readable form. Even though there is much advancement in the field of character recognition using machine learning techniques, recognition of handwritten MODI script, which is an ancient Indian script, is still in its infancy. It is due to the complex nature of the script that includes similar shapes of character and the absence of demarcation between words. MODI was an official language used to write Marathi. Deep learning-based models are very efficient in character recognition tasks and in this work an ACNN model is proposed using the on-the-fly data augmentation method and convolution neural network. The augmentation of the data will add variability and generalization to the data set. CNN has special convolution and pooling layers which have helped in better feature extraction of the characters. The performance of the proposed method is compared with the most accurate MODI character recognition method reported so far and it is found that the proposed method outperforms the other method. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.
- Source
- Lecture Notes in Networks and Systems, Vol-132, pp. 201-209.
- Date
- 2021-01-01
- Publisher
- Springer
- Subject
- Convolutional neural network; Data augmentation; Handwritten character recognition; MODI script
- Coverage
- Joseph S., CHRIST (Deemed to Be University), Bengaluru, India, Carmel College of Arts, Science and Commerce for Women, Nuvem, Goa, India; Datta A., CHRIST (Deemed to Be University), Bengaluru, India; Anto O., CHRIST (Deemed to Be University), Bengaluru, India; Philip S., CHRIST (Deemed to Be University), Bengaluru, India; George J., CHRIST (Deemed to Be University), Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Joseph S.; Datta A.; Anto O.; Philip S.; George J., “Ocr system framework for modi scripts using data augmentation and convolutional neural network,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/18783.