| |
| |
|
|
|
|
| The Heuristic Extraction Algorithms for Freeman Chain Code of Handwritten Character
|
|
Full
text: |
PDF(307KB) |
|
|
Source |
International Journal of Experimental Algorithms (IJEA) |
|
Table of Contents |
|
|
Download
Complete Issue PDF(0 Bytes) |
|
Volume: 1 Issue: 1 |
| |
Pages: |
|
Publication
Date: January / February |
|
ISSN
(Online): 2180-1282 |
|
|
|
|
|
Pages |
1 - 20 |
|
Author(s) |
|
|
|
Published
Date |
08-02-2011 |
|
Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
|
ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
| |
|
| |
KEYWORDS: Response Surface Methodology, Particle Swarm Optimization, Rhythmic Movements, Biped Robot, Central Pattern Generator, Van Der Pol Oscillators |
|
|
| |
|
|
| No
record found |
| |
|
| |
|
|
| Handwriting character recognition (HCR) is the ability of a computer to receive and interpret handwritten input. In HCR, there are many representation schemes and one of them is Freeman chain code (FCC). Chain code is a sequence of code direction of a characters and connection to a starting point which is often used in image processing. The main problem in representing character using FCC that it is depends on the starting points. Unfortunately, the study about FCC extraction using one continuous route and to minimizing the length of chain code to FCC from a thinned binary image (TBI) have not been widely explored. To solve this problem, heuristic algorithms are proposed to extract the FCC that is correctly representing the characters. This paper proposes two heuristics algorithm that are based on randomized and enumeration-based algorithms to solve the problems. As problem solving techniques, the randomized algorithm makes the random choices while enumeration-based algorithm enumerates all possible candidates for solution. The performance measures of the algorithms are the route length and computation time. The experiment on the algorithms are performed based on the chain code representation derived from established previous works of Center of Excellence for Document Analysis and Recognition (CEDAR) dataset which consists of 126 upper-case letter characters. The experimental result shows that route length of both algorithms are similar but the computation time of enumeration-based algorithm is higher than randomized algorithm. This is because enumeration-based algorithm considers all branches in route walk. |
| |
|
| |
|
| |
| 1 |
Suliman, A. Shakil, A. Sulaiman, M. N. Othman, M. and Wirza, R. “Hybrid of HMM and fuzzy logic for handwritten character recognition”. In Proceedings of Information Technology. Kuala Lumpur, Malaysia, 2008 |
|
|
| 2 |
Shi-Fei, D. Wei-Kuan, J. Chun-Yang, S. and Zhong-Zhi, S. “Research of pattern feature extraction and selection”. In Proceedings of Machine Learning and Cybernetics. Kunming, China, 2008 |
|
|
| 3 |
Zhaoqi, B. and Xuegong, Z. “Pattern Recognition”, 2nd Edition, Tsinghua University Press., (2000) |
|
|
| 4 |
Shi-Fei, D. Zhong-Zhi, S. Vun-Cheng, W. and Shu-Shan, L. “A novel feature extraction algorithm”. In Proceedings of Machine Learning and Cybernetics. Guangzhou, China, 2005 |
|
|
| 5 |
Jin, F. X. Wang, T. X. and Du, Z. X. “Pattern recognition on surveying data information analysis”. Nonferrous Metal, 9(2):425-430, 1999 |
|
|
| 6 |
Ding, S. F. and Jin, F. X. “Information features K-L transform based on information entropy”. Transactions Nonferrous Metals Society, 13(3):729-734. 2003 |
|
|
| 7 |
Turk, M. and Pentland, A. “Face recognition using eigenfaces”. In Proceedings of Computer Vision and Pattern Recognition. Maui, HI , USA, 1991 |
|
|
| 8 |
Freeman, H. “Techniques for the digital computer analysis of chain-encoded arbitrary plane curves”. In Proceedings of Electron. 1961 |
|
|
| 9 |
Liu, Y. K. and Zalik, B. “An efficient chain code with huffman coding”. Pattern Recognition, 38(4):553-557, 2005 |
|
|
| 10 |
Sánchez-Cruz, H. Bribiesca, E. and Rodríguez-Dagnino, R. M. “Efficiency of chain code to represent binary objects”. Pattern Recognition, 40(6):1660-1674, 2007 |
|
|
| 11 |
Wulandhari, L. A. and Haron, H. “The evolution and trend of chain code scheme”. Graphics, Vision and Image Processing, 8(3):17-23, 2008 |
|
|
| 12 |
Gyeonghwan, K. and Govindaraju, V. “Handwritten word recognition for real-time applications”. In Proceedings of the 3rd Document Analysis and Recognition. Montreal, Que. Canada, 1995 |
|
|
| 13 |
Hung, Y. “A chain coding approach for real-time recognition of on-line handwritten characters”. In Proceedings of Acoustics, Speech and Signal Processing. Atlanta, GA , USA, 1996 |
|
|
| 14 |
Guang-Rong, J. Guo-Yu, W. Houkes, Z. Bing, Z. and Yan-Ping, H. “A new method for fast computation of moments based on 8-neighbor chain code applied to 2-D object recognition”. In Proceedings of Intelligent Processing System. Beijing, China, 1997 |
|
|
| 15 |
Madhvanath, S. and Govindaraju, V. “Contour-based image preprocessing for holistic handwritten word recognition”. In Proceedings of the 4th Document Analysis and Recognition. Ulm, Germany, 1997. |
|
|
| 16 |
Martinez, J. C. de J. Lopez, J. and Luna Rosas, F. J. “A low-cost system for signature recognition. In Proceedings of the 42nd Circuit and System. Las Cruces, NM , USA, 1999 |
|
|
| 17 |
Khalifa, I. H. Fahmi, M. S. H. Hassanien, A. E. and Elsalamony, H. A. R. M. “Shape signature for recognition process”. In Proceedings of the 7th Radio Science. Minufiya, Egypt, 2000 |
|
|
| 18 |
Andrieux, J. and Seni, G. “Coding efficiency of multi-ring and single-ring differential chain coding for telewriting application”. Vision, Image and Signal Processing, 148(4):241-247, 2001 |
|
|
| 19 |
Mahmud, J. U. Raihan, M. F. and Rahman, C. M. “A complete OCR system for continuous Bengali characters”. In Proceedings of Convergent Technologies. Asia-Pacific Region, 2003 |
|
|
| 20 |
Chalechale, A. and Mertins, A. “Line segment distribution of sketches for Persian signature recognition”. In Proceedings of Convergent Technologies. Asia-Pacific Region, 2003 |
|
|
| 21 |
Paul, R. Nasif, M. S. and Farhad, S. M. “Fingerprint recognition by chain coded string matching techniques”. In Proceeding of Information and Communication Technology. Dhaka, Bangladesh, 2007 |
|
|
| 22 |
Alaei, A. Pal, U. and Nagabhushan, P. “Using modified contour features and svm based classifier for the recognition of persian/arabic handwritten numerals”. In Proceedings of the 7th Advances in Pattern Recognition. Kolkata, India, 2009 |
|
|
| 23 |
Siddiqi, I. and Vincent, N. “A set of chain code based features for writer recognition”. In Proceedings of Document Analysis and Recognition. Barcelona, 2009 |
|
|
| 24 |
Hasan, H. Haron, H. and Hashim, S. Z. “Freeman chain code extraction using differential evolution (de) and particle swarm optimization (pso)”. In Soft Computing and Pattern Recognition. Melaka, Malaysia, 2009 |
|
|
| 25 |
Nasien, D. Haron, H. and Yuhaniz, S. S. “Support vector machine (svm) for english handwritten character recognition”. In Proceedings of Computer Engineering and Application. Bali, Indonesia, 2010 |
|
|
| 26 |
Rehman, M. A. U. “A new scale invariant optimized chain code for nastaliq character representation”. In Proceedings of the 2nd Computer Modeling and Simulation. Sanya, Hainan, 2010 |
|
|
| 27 |
Engkamat, A. A. “Enhancement of Parallel Thinning Algorithm for Handwritten Characters Using Neural Network”. MSc Thesis, Universiti Teknologi Malaysia, November 2005. |
|
|
| 28 |
Atici, A. A. and Yarman-Vural, F. T. “A heuristic algorithm for optical character recognition of arabic script”. Signal Processing, 62(1):87-99. 1997 |
|
|
| 29 |
Hamid, A. and Haraty, R. “A neuro-heuristic approach for segmenting handwritten Arabic text”. In Proceedings of Computer Systems and Applications. Beirut, Lebanon, 2001 |
|
|
| 30 |
Matthews, P. C, Blessing, L. T. M, and Wallace, K. M. “The introduction of a design heuristics extraction method”. Advanced Engineering Informatics, 16(1):3-19. 2002 |
|
|
| 31 |
Yang, C. C and Li, K. W. “Segmenting chinese unknown words by heuristic method”. In Proceedings of the 6th Asian Digital Libraries. Kuala Lumpur, Malaysia, 2004 |
|
|
| 32 |
Yang, C. C and Li, K. W. “A heuristic method based on a statistical approach for chinese text segmentation”. American Society for Information Science and Technology, 6(13): 1438- 1447. 2005 |
|
|
| 33 |
Huang, J. S. and Chuang, K. (1986). “Heuristic approach to handwritten numeral recognition”. Pattern Recognition, 19(1):15-19. 1986 |
|
|
| 34 |
Wegener, I. “Towards a Theory of The randomized-based Search Heuristics”. Lecturer Notes in Computer Science, 2747:125-141 |
|
|
| 35 |
Hempel, H. “The randomized-based Algorithms and Complexity Theory”. Computer Science, 12(6):746-761, 2006 |
|
|
| 36 |
Sipser, M. “Time Complexity: Introduction to the Theory of Computation, 2nd Edition, Course Technology., pp. 1-400 (2005) |
|
|
| |
|
| |
|
| |
| |
|
| |
|
| |
| |
|
| |
|
| |
|
| Dewi Nasien : Colleagues
|
|
| Habibollah Haron : Colleagues
|
|
| Siti Sophiayati Yuhaniz : Colleagues
|
|
|
|
|
|
|
|
|
|
|