Manuscript Number : CSEIT183844
Zone-Wise Segmentation and Lexicon-Driven Recognition for Printed Myanmar Characters
Authors(2) :-Chit San Lwin, Xiangqian Wu This paper presents a new segmentation and recognition algorithms for Myanmar script inputted from offline printed images. Zone segmentation considers horizontal and vertical zones; it is applied to segment letters according to their roles such as primary or peripheral characters. In doing so, statistical and structural features of segmented characters are explored and exploited in recognition process. Hidden Markov model is used for recognition of primary characters while Kohonen self-organization map is used for peripheral characters. The recognized characters by each model are then combined, and finally are recognized by k-nearest neighbors algorithm with the help of lexicon is composed of all common Myanmar characters. Our OCR system for Myanmar characters tested on a dataset that approximately contains 7560 compounded characters. From the results, our system achieves higher significant results both segmentation and recognition compared to the other contemporary Myanmar OCR’s approaches.
Chit San Lwin Character Segmentation, Hidden Markov Model, Self-organization Map, k-nearest Neighbors, Lexicon Publication Details Published in : Volume 3 | Issue 8 | November-December 2018 Article Preview
Department of Mathematics, Monywa University, Monywa City, Sagaing Region, Myanmar
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, P. R. China
Xiangqian Wu
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, P. R. China
Date of Publication : 2018-11-30
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 161-180
Manuscript Number : CSEIT183844
Publisher : Technoscience Academy
Journal URL : https://res.ijsrcseit.com/CSEIT183844
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