Multilevel Classifiers in Recognition of Handwritten Kannada Numerals
The recognition of handwritten numeral is an
important area of research for its applications in post office, banks
and other organizations. This paper presents automatic recognition of
handwritten Kannada numerals based on structural features. Five
different types of features, namely, profile based 10-segment string,
water reservoir; vertical and horizontal strokes, end points and
average boundary length from the minimal bounding box are used in
the recognition of numeral. The effect of each feature and their
combination in the numeral classification is analyzed using nearest
neighbor classifiers. It is common to combine multiple categories of
features into a single feature vector for the classification. Instead,
separate classifiers can be used to classify based on each visual
feature individually and the final classification can be obtained based
on the combination of separate base classification results. One
popular approach is to combine the classifier results into a feature
vector and leaving the decision to next level classifier. This method
is extended to extract a better information, possibility distribution,
from the base classifiers in resolving the conflicts among the
classification results. Here, we use fuzzy k Nearest Neighbor (fuzzy
k-NN) as base classifier for individual feature sets, the results of
which together forms the feature vector for the final k Nearest
Neighbor (k-NN) classifier. Testing is done, using different features,
individually and in combination, on a database containing 1600
samples of different numerals and the results are compared with the
results of different existing methods.
[1] U. Pal,B B Chaudhuri, "Indain script character recognition: a survey",
pattern Recognition 37, pp 1887-1899, 2004.
[2] Liu C. L., Nakashimga, K. Sako, H. Fujisasa, "Handwritten Digit
Recognition: Benchmarking of the state-of-the-art techniques", Pattern
Recognition 36, 2003, pp 2271-2285.
[3] Anil K Jain and Torfinn Taxt, "Feature Extraction Methods for
Character Recognition: A Survey", Pattern Recognition, Vol 29, No, 4,
1996, pp 641- 662.
[4] Tuan A Mai and Ching Y Suen "A Generalized Knowledge based
System for the Recognition of Unconstrained HandWritten Numerals",
IEEE Trans. on Systems, Man and Cybernetics, vol. 20, no. 4, July /
August 1990.
[5] Govindan V K, Shivaprasad A P, "Character Recognition - a review",
Pattern Recognition 23,pp 671-683. 1990.
[6] Plamondon R, Srihari S N., " Online and Offline Handwriting
Recognition: Comprehensive Survey", IEEE Trans. Pattern Anal.
Machine Intell. PAMI 22,pp 63-84, 2000.
[7] U. Pal and P. P. Roy, "Multi-oriented and curved text lines extraction
from Indian documents", IEEE Trans. on Systems, Man and
Cybernetics - Part B, vol.34, pp.1676-1684, 2004.
[8] Leena lepistö, Iivari Kunttu,Jorma Autio and Ari Visa," Combining
Classifiers in Rock Image Classification - Supervised and Unsupervised
Approach", ICIAP 2005.
[9] J. M. Keller, M. R. Gray, and J. A. Givens, Jr., "A Fuzzy K-Nearest
Neighbor Algorithm", IEEE Transactions on Systems, Man, and
Cybernetics, Vol. 15, No. 4, pp. 580-585.
[10] R.O. Duda, P.E.Hart, and D.G. Stork, Pattern Classifica-tion, 2nd ed.,
John Wiley & Sons, New York, 2001.
[11] Dinesh Acharya U, N V Subba Reddy, Krishnamoorthy," Isolated
Kannada Numeral Recognition using Structural Features and K-Means
Cluster", Proc. of IISN 2007, pp. 125-129.
[12] G.G.Rajaput and Mallikarjun Hangarge, "Recognition of Isolated
Handwritten Kannada Numeral Recognition Using Image Fusion
Method-, PREMI07, LNCS 4815, pp 153-160.
[13] V N Manjunath Ardhya, G Hemanth Kumar and S Noushath, "Robust
Unconstrained Handwritten Digit Recognition Using Radon Transform",
IEEE-ICSN 2007, pp. 626-629.
[14] Rajashekaradhya S V and P Vanaja Ranjan, "Isolated Handwritten
Kannada Digit Recognition: A Novel Approach", ICCR08, pp.134-140.
[15] Ganapatsingh G Rajput, "Unconstrained Kannada Handwritten Numeral
Recognition based upon Image Reduction and KNN classifier", ICCR08,
pp.11-16.
[16] N. Sharma, U. Pal, and F. Kimura, "Recognition of Handwritten
Kannada Numerals", Proc, of IEEE-ICIT 2006.
[17] L. Lepistö, I. Kunttu, J. Autio, and A. Visa, "Classifica-tion of Nonhomogenous
Textures by Combining Classi-fiers", Proceedings of IEEE
ICIP, Vol. 1, 2003, pp. 981-984.
[18] J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas, "On Combining
Classifiers", IEEE Trans. on Pattern Analysis and Machine Intelligence,
Vol. 20, No. 3, March 1998, pp. 226-239.
[19] L. Xu, A. Krzyzak, and C. Y. Suen, "Methods for Com-bining Multiple
Classifiers and their Applications to Handwriting Recognition", IEEE
Trans. on Systems, Man, and Cybernetics, Vol. 22, No. 3, 1992, pp. 418-
435.
[20] A. K. Jain, S. Prabhakar, and S. Chen, "Combining Multi-ple Matchers
for a High Security Fingerprint Verification System", Pattern
Recognition Letters, Vol. 20, No. 11-13, Nov. 1999, pp. 1371-1379.
[21] L. Lam, C.Y. Suen, "Application of majority voting to pattern
recognition: An analysis of the behavior and performance", IEEE
Transactions on Systems, Man, and Cy-bernetics, vol. 27, No. 5, 1997,
pp. 553-567.
[22] T. Vasudev, G Hemanthkumar,D S Guru and P Nagabhushan,
"Extension of 7-segment display concept for Handwritten Numeral
Recognition", Proc. of NCDAR, 200.1.
[23] P Nagabhushan and N V Subba Reddy, "A new approach to
Handwritten Numeral Recognition", Austrian Pattern Recognition
Society, 1996.
[24] U. Pal, B. B. Chowdhary, "Automatic Recognition of Unconstrained
Offline Bangla Handwritten Numerals", LNCS, Vol.1948, Springer,
2000, pp 371- 378.
[25] U Bhattacharya T. K. Das, A. Datta, S. K. Parui, and B. B. Caudhri,
"Recognition of Handprinted Bangla using Neural Network Models",
LNAI 2275, 2002, pp 228-235.
[26] P. M. Patil, T. T. Sotakke, "Scale and Translation Invariant Handwritten
Devanagaari Numeral Character Recognition using General Fuzzy
Neural Networks", Pattern Recognition 40, 2007, pp 2110-2117.
[27] Zohn Yen, Reza Langari, "Fuzzy Logic Intelligence, Control, and
Information", Pearson Education, 2003.
[1] U. Pal,B B Chaudhuri, "Indain script character recognition: a survey",
pattern Recognition 37, pp 1887-1899, 2004.
[2] Liu C. L., Nakashimga, K. Sako, H. Fujisasa, "Handwritten Digit
Recognition: Benchmarking of the state-of-the-art techniques", Pattern
Recognition 36, 2003, pp 2271-2285.
[3] Anil K Jain and Torfinn Taxt, "Feature Extraction Methods for
Character Recognition: A Survey", Pattern Recognition, Vol 29, No, 4,
1996, pp 641- 662.
[4] Tuan A Mai and Ching Y Suen "A Generalized Knowledge based
System for the Recognition of Unconstrained HandWritten Numerals",
IEEE Trans. on Systems, Man and Cybernetics, vol. 20, no. 4, July /
August 1990.
[5] Govindan V K, Shivaprasad A P, "Character Recognition - a review",
Pattern Recognition 23,pp 671-683. 1990.
[6] Plamondon R, Srihari S N., " Online and Offline Handwriting
Recognition: Comprehensive Survey", IEEE Trans. Pattern Anal.
Machine Intell. PAMI 22,pp 63-84, 2000.
[7] U. Pal and P. P. Roy, "Multi-oriented and curved text lines extraction
from Indian documents", IEEE Trans. on Systems, Man and
Cybernetics - Part B, vol.34, pp.1676-1684, 2004.
[8] Leena lepistö, Iivari Kunttu,Jorma Autio and Ari Visa," Combining
Classifiers in Rock Image Classification - Supervised and Unsupervised
Approach", ICIAP 2005.
[9] J. M. Keller, M. R. Gray, and J. A. Givens, Jr., "A Fuzzy K-Nearest
Neighbor Algorithm", IEEE Transactions on Systems, Man, and
Cybernetics, Vol. 15, No. 4, pp. 580-585.
[10] R.O. Duda, P.E.Hart, and D.G. Stork, Pattern Classifica-tion, 2nd ed.,
John Wiley & Sons, New York, 2001.
[11] Dinesh Acharya U, N V Subba Reddy, Krishnamoorthy," Isolated
Kannada Numeral Recognition using Structural Features and K-Means
Cluster", Proc. of IISN 2007, pp. 125-129.
[12] G.G.Rajaput and Mallikarjun Hangarge, "Recognition of Isolated
Handwritten Kannada Numeral Recognition Using Image Fusion
Method-, PREMI07, LNCS 4815, pp 153-160.
[13] V N Manjunath Ardhya, G Hemanth Kumar and S Noushath, "Robust
Unconstrained Handwritten Digit Recognition Using Radon Transform",
IEEE-ICSN 2007, pp. 626-629.
[14] Rajashekaradhya S V and P Vanaja Ranjan, "Isolated Handwritten
Kannada Digit Recognition: A Novel Approach", ICCR08, pp.134-140.
[15] Ganapatsingh G Rajput, "Unconstrained Kannada Handwritten Numeral
Recognition based upon Image Reduction and KNN classifier", ICCR08,
pp.11-16.
[16] N. Sharma, U. Pal, and F. Kimura, "Recognition of Handwritten
Kannada Numerals", Proc, of IEEE-ICIT 2006.
[17] L. Lepistö, I. Kunttu, J. Autio, and A. Visa, "Classifica-tion of Nonhomogenous
Textures by Combining Classi-fiers", Proceedings of IEEE
ICIP, Vol. 1, 2003, pp. 981-984.
[18] J. Kittler, M. Hatef, R.P.W. Duin, and J. Matas, "On Combining
Classifiers", IEEE Trans. on Pattern Analysis and Machine Intelligence,
Vol. 20, No. 3, March 1998, pp. 226-239.
[19] L. Xu, A. Krzyzak, and C. Y. Suen, "Methods for Com-bining Multiple
Classifiers and their Applications to Handwriting Recognition", IEEE
Trans. on Systems, Man, and Cybernetics, Vol. 22, No. 3, 1992, pp. 418-
435.
[20] A. K. Jain, S. Prabhakar, and S. Chen, "Combining Multi-ple Matchers
for a High Security Fingerprint Verification System", Pattern
Recognition Letters, Vol. 20, No. 11-13, Nov. 1999, pp. 1371-1379.
[21] L. Lam, C.Y. Suen, "Application of majority voting to pattern
recognition: An analysis of the behavior and performance", IEEE
Transactions on Systems, Man, and Cy-bernetics, vol. 27, No. 5, 1997,
pp. 553-567.
[22] T. Vasudev, G Hemanthkumar,D S Guru and P Nagabhushan,
"Extension of 7-segment display concept for Handwritten Numeral
Recognition", Proc. of NCDAR, 200.1.
[23] P Nagabhushan and N V Subba Reddy, "A new approach to
Handwritten Numeral Recognition", Austrian Pattern Recognition
Society, 1996.
[24] U. Pal, B. B. Chowdhary, "Automatic Recognition of Unconstrained
Offline Bangla Handwritten Numerals", LNCS, Vol.1948, Springer,
2000, pp 371- 378.
[25] U Bhattacharya T. K. Das, A. Datta, S. K. Parui, and B. B. Caudhri,
"Recognition of Handprinted Bangla using Neural Network Models",
LNAI 2275, 2002, pp 228-235.
[26] P. M. Patil, T. T. Sotakke, "Scale and Translation Invariant Handwritten
Devanagaari Numeral Character Recognition using General Fuzzy
Neural Networks", Pattern Recognition 40, 2007, pp 2110-2117.
[27] Zohn Yen, Reza Langari, "Fuzzy Logic Intelligence, Control, and
Information", Pearson Education, 2003.
@article{"International Journal of Information, Control and Computer Sciences:50984", author = "Dinesh Acharya U. and N. V. Subba Reddy and Krishnamoorthi Makkithaya", title = "Multilevel Classifiers in Recognition of Handwritten Kannada Numerals", abstract = "The recognition of handwritten numeral is an
important area of research for its applications in post office, banks
and other organizations. This paper presents automatic recognition of
handwritten Kannada numerals based on structural features. Five
different types of features, namely, profile based 10-segment string,
water reservoir; vertical and horizontal strokes, end points and
average boundary length from the minimal bounding box are used in
the recognition of numeral. The effect of each feature and their
combination in the numeral classification is analyzed using nearest
neighbor classifiers. It is common to combine multiple categories of
features into a single feature vector for the classification. Instead,
separate classifiers can be used to classify based on each visual
feature individually and the final classification can be obtained based
on the combination of separate base classification results. One
popular approach is to combine the classifier results into a feature
vector and leaving the decision to next level classifier. This method
is extended to extract a better information, possibility distribution,
from the base classifiers in resolving the conflicts among the
classification results. Here, we use fuzzy k Nearest Neighbor (fuzzy
k-NN) as base classifier for individual feature sets, the results of
which together forms the feature vector for the final k Nearest
Neighbor (k-NN) classifier. Testing is done, using different features,
individually and in combination, on a database containing 1600
samples of different numerals and the results are compared with the
results of different existing methods.", keywords = "Fuzzy k Nearest Neighbor, Multiple Classifiers,Numeral Recognition, Structural features.", volume = "2", number = "6", pages = "1836-6", }
