Myanmar Character Recognition Using Eight Direction Chain Code Frequency Features
Character recognition is the process of converting a text image file into editable and searchable text file. Feature Extraction is the heart of any character recognition system. The character recognition rate may be low or high depending on the extracted features. In the proposed paper, 25 features for one character are used in character recognition. Basically, there are three steps of character recognition such as character segmentation, feature extraction and classification. In segmentation step, horizontal cropping method is used for line segmentation and vertical cropping method is used for character segmentation. In the Feature extraction step, features are extracted in two ways. The first way is that the 8 features are extracted from the entire input character using eight direction chain code frequency extraction. The second way is that the input character is divided into 16 blocks. For each block, although 8 feature values are obtained through eight-direction chain code frequency extraction method, we define the sum of these 8 feature values as a feature for one block. Therefore, 16 features are extracted from that 16 blocks in the second way. We use the number of holes feature to cluster the similar characters. We can recognize the almost Myanmar common characters with various font sizes by using these features. All these 25 features are used in both training part and testing part. In the classification step, the characters are classified by matching the all features of input character with already trained features of characters.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1316857Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 354
 T. Swe and P. Tin, 2006, “Recognition and Translation of Myanmar Printed Text based on Hopefield Neural Network” IEEE.
 S. S. Mon and M. M. Sein, 2006, “Recognition of Myanmar Handwriting Text Based on Hidden Markov Model”.
 E. E. Phyu, Z. C. Aye, E. P. Khaing and Y. Thein, 2007, “Recognition of Myanmar Handwritten Compound Words based on MICR”.
 Z. C. Aye, E. E. Phyu, Y. Thein and M. M. Sein,2008, “Myanmar Intelligent Character Recognition (MICR) and Myanmar Voice Mixer (MVM) System”.
 E. Theingi, E. K. Khine, T. W. K. kyaw, Y. Thein, 2009, Enhance the Handwritten Myanmar Characters Recognition System for Pali based on MICR”.
 Y. Thein and S. S. S. Yee, 2010, “High Accuracy Myanmar Handwritten Character Recognition using Hybrid approach through MICR and Neural Network”, IJCSI International Journal of Computer Science Issues, Vol. 7, Issue 6, November, ISSN(online):1694-0814.
 Y. Thein and S.S.S Yee, 2010, “Online Myanmar Handwritten Compound Words Recognition and Erratum Detection with MICR”.
 H. P. P. Win, P. T. T. Khine and K. N. N. Tun, 2011 “Bilingual OCR System for Myanmar and English Scripts with Simutaneous Recognition”, International Journal of Scientific & Engineering Research Volume 2, Issue 10, October, ISSN 2229-5518.
 T. Htike and Y. Thein, 2013, “Handwritten Character Recognition Using Competitive Neural Trees”, IACSIT International Journal of Engineering and Technology, Vol. 5, No. 3, June 2013.
 Thuzar Tint, and Nyein Aye, 2014, “Myanmar Text Area Identification from Video Scenes”, International Conference on Advanced in Engineering and Technology (ICAET2014), March, Singapore.
 Emmanuel, Rosemol, and Jilu George. "Automatic detection and recognition of Malayalam text from natural scene images." IOSR Journal of VLSI and Signal Processing 3.2 (2013): 55-61.
 Sok, Pongsametrey, and Nguonly Taing. "Support Vector Machine (SVM) Based Classifier For Khmer Printed Character-set Recognition." Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA). IEEE, 2014.
 Hassaballah, M., Aly Amin Abdelmgeid, and Hammam A. Alshazly. "Image Features Detection, Description and Matching." Image Feature Detectors and Descriptors. Springer International Publishing, 2016. 11-45.