This Article 
 Bibliographic References 
 Add to: 
Extraction and Optimization of B-Spline PBD Templates for Recognition of Connected Handwritten Digit Strings
January 2002 (vol. 24 no. 1)
pp. 132-139

Recognition of connected handwritten digit strings is a challenging task due mainly to two problems: poor character segmentation and unreliable isolated character recognition. In this paper, we first present a rational B-spline representation of digit templates based on Pixel-to-Boundary Distance (PBD) maps. We then present a neural network approach to extract B-spline PBD templates and an evolutionary algorithm to optimize these templates. In total, 1,000 templates (100 templates for each of 10 classes) were extracted from and optimized on 10,426 training samples from the NIST Special Database 3. By using these templates, a nearest neighbor classifier can successfully reject 90.7 percent of nondigit patterns while achieving a 96.4 percent correct classification of isolated test digits. When our classifier is applied to the recognition of 4,958 connected handwritten digit strings (4,555 2-digit, 355 3-digit, and 48 4-digit strings) from the NIST Special Database 3 with a dynamic programming approach, it has a correct classification rate of 82.4 percent with a rejection rate of as low as 0.85 percent. Our classifier compares favorably in terms of correct classification rate and robustness with other classifiers that are tested.

[1] M. Shridhar and A. Badreldin, “Context-Directed Segmentation Algorithm for Handwritten Numeral Strings,” Image Vision Computing, vol. 5, no. 1, pp. 3-9, 1987.
[2] Z. Shi and V. Govindaraju, “Segmentation and Recognition of Connected Handwritten Numeral Strings,” Pattern Recognition, vol. 30, no. 9, pp. 1501-1504, 1997.
[3] Z.K. Lu, Z. Chi, W.C. Siu, and P.F. Shi, “A Background-Thinning Based Algorithm for Separating Handwritten Touching Digit Strings,” Pattern Recognition, vol. 32, no. 6, pp. 921-933, 1999.
[4] J. Rocha and T. Pavlidis, "Character Recognition Without Segmentation" IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 17, pp. 903-909, 1995.
[5] D.M. Ha, M. Zimmermann, and H. Bunke, “Off-Line Handwritten Numeral String Recognition by Combining Segmentation-Based and Segmentation-Free Methods,” Pattern Recognition, vol. 31, no. 3, pp. 257-272, 1998.
[6] G. Kim and V. Govindaraju, “A Lexicon Driven Approach to Handwritten Word Recognition for Real Time Applications,“ IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 4, pp. 366-379, Apr. 1997.
[7] P.D. Gader, M. Mohammed, and J.H. Chiang, “Handwritten Word Recognition with Character and Inter Character Neural Networks,” IEEE Trans. System, Man, and Cybernetics, pp. 158-164, vol. 27, no. 1, 1997.
[8] V.K. Govindan and A.P. Shivaprasad, “Character Recognition: A Review,” Pattern Recognition, vol. 23, no. 7, pp. 671-683, 1990.
[9] R. Impedovo, L. Ottaviano, and S. Occhinegro, “Optical Character Recognition—A Survey,” Int'l J. Pattern Recognition and Artificial Intelligence, vol. 5, nos. 1 and 2, pp. 1-24, 1991.
[10] Q.D. Trier, A.K. Jain, and T. Taxt, “Feature Extraction Methods for Character Recognition—A Survey,” Pattern Recognition, vol. 29, no. 4, pp. 641-662, 1996.
[11] H. Yan, “Prototype Optimization of a Nearest Neighbor Classifier Using a Multi-Layer Neural Network,” Pattern Recognition, vol. 26, pp. 317-324, 1993.
[12] T. Wakahara, "Shape Matching Using LAT and Its Application to Handwritten Numeral Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 16, no. 6, pp. 618-629, June 1994.
[13] K.W. Cheung, D.Y. Yeung, and R.T. Chin, “A Unified Framework for Handwritten Character Recognition Using Deformable Models,” Proc. Second Asian Conf. Computer Vision, vol. 1, pp. 344-348, 1995.
[14] A.K. Jain and D. Zongker, "Representation and Recognition of Handwritten Digits Using Deformable Templates," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 12, pp. 1,386-1,390, Dec. 1997.
[15] K. Versprille, Computer Aided Design Applications of the Rational B-Spline Approximation Form, doctoral dissertation, University of Syracuse, New York, 1975.
[16] M. Revow, C.K.I. Williams, and G.E. Hinton, “Using Generative Models for Handwritten Digit Recognition,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, no. 6, pp. 592-606, June 1996.
[17] T. Baeck and H.-P. Schwefel, “An Overview of Evolutionary Algorithms for Parameter Optimization,” Evolutionary Computation, vol. 1, no.1, pp. 1-24, 1993.
[18] D. Fogel, "An Introduction to Simulated Evolutionary Optimization," IEEE Trans. Neural Networks, vol. 5, pp. 3-14, Jan. 1994.
[19] T. Kozek, T. Roska, and L.O. Chua, “Genetic Algorithm for CNN Template Learning,” IEEE Trans. Circuits and Systems—I: Fundamental, Theory, and Applications, vol. 40, no. 6, pp. 392-402, 1993.
[20] K. Delibasis and P. Undrill, “Genetic Algorithm and Deformable Geometric Models for Anatomical Object Recognition,” Proc. IEE Colloquium Genetic Algorithm in Image Processing and Vision, vol. 8, pp. 1-7, 1994.
[21] M. Sarkar, B. Yegnanarayana, and D. Khemani, “A Clustering Algorithm Using an Evolutionary Programming-Based Approach,” Pattern Recognition Letters, vol. 18, pp. 975-986, 1997.
[22] D. Bouchaffra, V. Govindaraju, and S.N. Srihari, “Postprocessing of Recognized Strings Using Nonstationary Markovian Models,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 10, pp. 990-999, Oct. 1999.

Index Terms:
Connected handwritten digit recognition, pixel-to-boundary distance map, B-spline fitting, digit templates, template optimization, nearest neighbor classifier, multilayer perceptron classifier, evolutionary algorithm.
Zhongkang Lu, Zheru Chi, Wan-Chi Siu, "Extraction and Optimization of B-Spline PBD Templates for Recognition of Connected Handwritten Digit Strings," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 132-139, Jan. 2002, doi:10.1109/34.982890
Usage of this product signifies your acceptance of the Terms of Use.