Off-Line Hand Written Thai Character Recognition using Ant-Miner Algorithm
Much research into handwritten Thai character
recognition have been proposed, such as comparing heads of
characters, Fuzzy logic and structure trees, etc. This paper presents a
system of handwritten Thai character recognition, which is based on
the Ant-minor algorithm (data mining based on Ant colony
optimization). Zoning is initially used to determine each character.
Then three distinct features (also called attributes) of each character
in each zone are extracted. The attributes are Head zone, End point,
and Feature code. All attributes are used for construct the
classification rules by an Ant-miner algorithm in order to classify
112 Thai characters. For this experiment, the Ant-miner algorithm is
adapted, with a small change to increase the recognition rate. The
result of this experiment is a 97% recognition rate of the training set
(11200 characters) and 82.7% recognition rate of unseen data test
(22400 characters).
[1] P. Choruengwiwat, "Thai handwritten character recognition using
extraction of distinctive features," Masters Thesis, Department of
Electrical Engineering, Chulalongkorn University, 1998.
[2] T. Thongkamwitoon , W. Asdornwised, S. Aramvith, S. Jitapunkul,
"On-line Thai-English handwritten character recognition using
distinctive features," APCCAS '02. 2002 Asia-Pacific Conference on
Circuits and Systems, vol. 2, p 259 - 264, 2002.
[3] S. Airphaiboon, "Recognition of Hand-written Thai character
considering the head of character," Masters Thesis, Department of
Electrical Engineering, King Monkut-s Institute of Technology
Ladkrabang, Bangkok, Thailand, 1988.
[4] Parpinelli R.S, Lopes H.S, Freitas, A.A., "Data mining with an ant
colony optimization algorithm," IEEE Transactions on Evolutionary
Computation, p321 - 332, 2002.
[5] Bo Liu, Abbas H.A, McKay B., "Classification rule discovery with
ant colony optimization," IEEE/WIC International Conference on
Intelligent Agent Technology, p 83 - 88, 2003.
[6] Marco Dorigo, Thomas Stutzle, "Ant colony optimization", A Bradford
Book The MIT Press, Cambridge, Massachusetts, London, England,
2004.
[1] P. Choruengwiwat, "Thai handwritten character recognition using
extraction of distinctive features," Masters Thesis, Department of
Electrical Engineering, Chulalongkorn University, 1998.
[2] T. Thongkamwitoon , W. Asdornwised, S. Aramvith, S. Jitapunkul,
"On-line Thai-English handwritten character recognition using
distinctive features," APCCAS '02. 2002 Asia-Pacific Conference on
Circuits and Systems, vol. 2, p 259 - 264, 2002.
[3] S. Airphaiboon, "Recognition of Hand-written Thai character
considering the head of character," Masters Thesis, Department of
Electrical Engineering, King Monkut-s Institute of Technology
Ladkrabang, Bangkok, Thailand, 1988.
[4] Parpinelli R.S, Lopes H.S, Freitas, A.A., "Data mining with an ant
colony optimization algorithm," IEEE Transactions on Evolutionary
Computation, p321 - 332, 2002.
[5] Bo Liu, Abbas H.A, McKay B., "Classification rule discovery with
ant colony optimization," IEEE/WIC International Conference on
Intelligent Agent Technology, p 83 - 88, 2003.
[6] Marco Dorigo, Thomas Stutzle, "Ant colony optimization", A Bradford
Book The MIT Press, Cambridge, Massachusetts, London, England,
2004.
@article{"International Journal of Information, Control and Computer Sciences:56189", author = "P. Phokharatkul and K. Sankhuangaw and S. Somkuarnpanit and S. Phaiboon and C. Kimpan", title = "Off-Line Hand Written Thai Character Recognition using Ant-Miner Algorithm", abstract = "Much research into handwritten Thai character
recognition have been proposed, such as comparing heads of
characters, Fuzzy logic and structure trees, etc. This paper presents a
system of handwritten Thai character recognition, which is based on
the Ant-minor algorithm (data mining based on Ant colony
optimization). Zoning is initially used to determine each character.
Then three distinct features (also called attributes) of each character
in each zone are extracted. The attributes are Head zone, End point,
and Feature code. All attributes are used for construct the
classification rules by an Ant-miner algorithm in order to classify
112 Thai characters. For this experiment, the Ant-miner algorithm is
adapted, with a small change to increase the recognition rate. The
result of this experiment is a 97% recognition rate of the training set
(11200 characters) and 82.7% recognition rate of unseen data test
(22400 characters).", keywords = "Hand written, Thai character recognition, Ant-mineralgorithm, distinct feature.", volume = "1", number = "8", pages = "2482-6", }