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A segmentation based adaptive approach for cursive handwritten text recognition

conference contribution
posted on 06.12.2017, 00:00 by Brijesh VermaBrijesh Verma, Hong Suk LeeHong Suk Lee
The paper presents a segmentation based adaptive approach for the learning and recognition of single person’s handwritten text. The approach is incorporated into an automated intelligent system for scanning of handwritten text on a paper and converting it into a text file. It scans an A4 size handwritten page and segments it into lines, words and characters. The segmented characters are passed to a neural classifier for the recognition. The final word is passed through a lexicon based matching process to improve the accuracy of the recognized text. Two neural networks are investigated for the learning of segmented characters quickly and accurately. The experimental results show that the proposed approach can produce high text recognition accuracy with a small number of training samples.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Parent Title

Proceedings of International Joint Conference on Neural Networks, Orlando, USA, 12-17 August 2007.

Start Page

2212

End Page

2216

Number of Pages

5

Start Date

01/01/2007

eISSN

1098-7576

ISBN-10

142441380X

Location

Orlando, USA

Publisher

IEEE

Place of Publication

Orlando, USA

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Business and Informatics;

Era Eligible

Yes

Name of Conference

IEEE International Joint Conference on Neural Networks