An Efficient Feature Extraction Algorithm for the Recognition of Handwritten Arabic Digits
In this paper, an efficient structural approach for
recognizing on-line handwritten digits is proposed. After reading
the digit from the user, the slope is estimated and normalized for
adjacent nodes. Based on the changing of signs of the slope values,
the primitives are identified and extracted. The names of these
primitives are represented by strings, and then a finite state
machine, which contains the grammars of the digits, is traced to
identify the digit. Finally, if there is any ambiguity, it will be
resolved. Experiments showed that this technique is flexible and
can achieve high recognition accuracy for the shapes of the digits
represented in this work.
[1] C. C. Tappert, C. Y. Suen, and T. Wakahara, "The state of the art in
on-line handwriting recognition", IEEE Trans. On Pattern Analysis
and Machine Intelligence, vol 12, no 8, pp. 787-808, 1990.
[2] R. G. Casey and E. Lecolinet, "Strategies in character segmentation:
A survey", In Proceedings of International Conference on Document
Analysis and Recognition, pp. 1028-1033, 1995.
[3] K. S. Fu, Syntactic Pattern Recognition and Applications, Prentice-
Hall, Englewood Cliffs, NJ, 1982.
[4] T. Pavlidis, Structural Pattern Recognition, Springer, New York,
1977.
[5] s. Lucas, E. Vidal, A. Amiri, S. Hanlon, and J.C. Amengual, "A
comparison of syntactic and statistical techniqes for off-line OCR",
in: R. C. Carrasco, J. Oncina (Eds.), Grammatical Inferrence and
Applications (ICGI-94), Springer, Berlin, pp. 168-179, 1994.
[6] Brijesh Verma, "A Contour Code Feature Based Segmentation For
Handwriting Recognition". Proceedings of the Seventh International
Conference on Document Analysis and Recognition (ICDAR 2003).
[7] Daekeun You and Gyeonghwan Kim, "An approach for locating
segmentation points of handwritten digit strings using a neural
network", Proceedings of the Seventh International Conference on
Document Analysis and Recognition (ICDAR 2003).
[8] Robert T. Olszewski, "Generalized Feature Extraction for Structural
Pattern Recognition in TimeSeries Data", PhD thesis, University-
Pittsburgh, 2001.
[9] Kam-Fai Chan and Dit-Yan Yeung, "An effecient syntactic approach
to structural analysis of on-line handwritten mathematical
expressions", Pattern Recognition, vol 33, pp. 375-384, 2000.
[10] Kam-Fai Chan and Dit-Yan Yeung, "Recognizing on-line
handwritten alphanumeric characters through flexible structural
matching", Pattern Recognition, vol 32, pp. 1099-1114, 1999.
[11] Adnan Amin, "Off-Line Arabic Character Recognition: The State Of
The Art", Pattern Recognition, 31(5), 517-530, (1998).
[12] Sven Behnke, Marcus Pfisher, and Raul Rojas, "A Study on the
Combination of Classifiers for Handwritten Didit Recognition",
Proceedings of Neural Networks in Applications, Third International
Workshop (NN'98), Magdeburg, Germany, pp. 39-46, 1998.
[13] Sven Behnke, Raul Rojas, and Marcus Pfister, "Recognition of
Handwritten Digits using Structural Information", Proceedings of
the International Conference of Nueral Network, Houston TX, vol 3,
pp. 1391-1396, 1997.
[14] S. Madhvanath, G. Kim, and V. Govindaraju, "Chaincode Contour
Processing for Handwritten Word Recognition", IEEE Trans. On
Pattern Analysis and Machine Intelligence, vol 21, no 9, pp. 928 932,
1999.
[15] Robert Schalkoff, Pattern Recognition: Statistical, Structural, and
Neural Approaches", John Wiley & Sons Inc. 1992.
[16] Rafael C. Gonzalez and Michael G. Thomason, Syntactic Pattern
Recognition: An Introduction, Addison Wesley, Reading,
Massachusetts, 1978.
[17] Richard O. Duda, Peter E. Hart, and David E. Stork. Pattern
Classification. Wiley, New York, second edition, 2001.
[18] Keinosuke Fukunaga. Introduction to Statistical Pattern
Recognition. Academic Press, Boston, second edition, 1990.
[19] Anil K. Jain, Robert P. W. Duin, and Jianchang Mao. Statistical
Pattern Recognition: A Review. IEEE Transactions on Pattern
Analysis and Machine Intelligence, 22(1):4 -37, January 2000.
[20] Robert T. Olszewski, "Generalized Feature Extraction for Structural
Pattern Recognition in TimeSeries Data", PhD. Thesis, 2001, School
of Computer Science, Carnegie Mellon University, Pittsburgh, PA
15213.
[1] C. C. Tappert, C. Y. Suen, and T. Wakahara, "The state of the art in
on-line handwriting recognition", IEEE Trans. On Pattern Analysis
and Machine Intelligence, vol 12, no 8, pp. 787-808, 1990.
[2] R. G. Casey and E. Lecolinet, "Strategies in character segmentation:
A survey", In Proceedings of International Conference on Document
Analysis and Recognition, pp. 1028-1033, 1995.
[3] K. S. Fu, Syntactic Pattern Recognition and Applications, Prentice-
Hall, Englewood Cliffs, NJ, 1982.
[4] T. Pavlidis, Structural Pattern Recognition, Springer, New York,
1977.
[5] s. Lucas, E. Vidal, A. Amiri, S. Hanlon, and J.C. Amengual, "A
comparison of syntactic and statistical techniqes for off-line OCR",
in: R. C. Carrasco, J. Oncina (Eds.), Grammatical Inferrence and
Applications (ICGI-94), Springer, Berlin, pp. 168-179, 1994.
[6] Brijesh Verma, "A Contour Code Feature Based Segmentation For
Handwriting Recognition". Proceedings of the Seventh International
Conference on Document Analysis and Recognition (ICDAR 2003).
[7] Daekeun You and Gyeonghwan Kim, "An approach for locating
segmentation points of handwritten digit strings using a neural
network", Proceedings of the Seventh International Conference on
Document Analysis and Recognition (ICDAR 2003).
[8] Robert T. Olszewski, "Generalized Feature Extraction for Structural
Pattern Recognition in TimeSeries Data", PhD thesis, University-
Pittsburgh, 2001.
[9] Kam-Fai Chan and Dit-Yan Yeung, "An effecient syntactic approach
to structural analysis of on-line handwritten mathematical
expressions", Pattern Recognition, vol 33, pp. 375-384, 2000.
[10] Kam-Fai Chan and Dit-Yan Yeung, "Recognizing on-line
handwritten alphanumeric characters through flexible structural
matching", Pattern Recognition, vol 32, pp. 1099-1114, 1999.
[11] Adnan Amin, "Off-Line Arabic Character Recognition: The State Of
The Art", Pattern Recognition, 31(5), 517-530, (1998).
[12] Sven Behnke, Marcus Pfisher, and Raul Rojas, "A Study on the
Combination of Classifiers for Handwritten Didit Recognition",
Proceedings of Neural Networks in Applications, Third International
Workshop (NN'98), Magdeburg, Germany, pp. 39-46, 1998.
[13] Sven Behnke, Raul Rojas, and Marcus Pfister, "Recognition of
Handwritten Digits using Structural Information", Proceedings of
the International Conference of Nueral Network, Houston TX, vol 3,
pp. 1391-1396, 1997.
[14] S. Madhvanath, G. Kim, and V. Govindaraju, "Chaincode Contour
Processing for Handwritten Word Recognition", IEEE Trans. On
Pattern Analysis and Machine Intelligence, vol 21, no 9, pp. 928 932,
1999.
[15] Robert Schalkoff, Pattern Recognition: Statistical, Structural, and
Neural Approaches", John Wiley & Sons Inc. 1992.
[16] Rafael C. Gonzalez and Michael G. Thomason, Syntactic Pattern
Recognition: An Introduction, Addison Wesley, Reading,
Massachusetts, 1978.
[17] Richard O. Duda, Peter E. Hart, and David E. Stork. Pattern
Classification. Wiley, New York, second edition, 2001.
[18] Keinosuke Fukunaga. Introduction to Statistical Pattern
Recognition. Academic Press, Boston, second edition, 1990.
[19] Anil K. Jain, Robert P. W. Duin, and Jianchang Mao. Statistical
Pattern Recognition: A Review. IEEE Transactions on Pattern
Analysis and Machine Intelligence, 22(1):4 -37, January 2000.
[20] Robert T. Olszewski, "Generalized Feature Extraction for Structural
Pattern Recognition in TimeSeries Data", PhD. Thesis, 2001, School
of Computer Science, Carnegie Mellon University, Pittsburgh, PA
15213.
@article{"International Journal of Information, Control and Computer Sciences:49417", author = "Ahmad T. Al-Taani", title = "An Efficient Feature Extraction Algorithm for the Recognition of Handwritten Arabic Digits", abstract = "In this paper, an efficient structural approach for
recognizing on-line handwritten digits is proposed. After reading
the digit from the user, the slope is estimated and normalized for
adjacent nodes. Based on the changing of signs of the slope values,
the primitives are identified and extracted. The names of these
primitives are represented by strings, and then a finite state
machine, which contains the grammars of the digits, is traced to
identify the digit. Finally, if there is any ambiguity, it will be
resolved. Experiments showed that this technique is flexible and
can achieve high recognition accuracy for the shapes of the digits
represented in this work.", keywords = "Digits Recognition, Pattern Recognition, FeatureExtraction, Structural Primitives, Document Processing,Handwritten Recognition, Primitives Selection.", volume = "2", number = "6", pages = "1770-5", }
