Local context in non-linear deformation models for handwritten character recognition

We evaluate different two-dimensional non-linear deformation models for handwritten character recognition. Starting from a true two-dimensional model, we derive pseudo-two-dimensional and zero-order deformation models. Experiments show that it is most important to include suitable representations of the local image context of each pixel to increase performance. With these methods, we achieve very competitive results across five different tasks, in particular 0.5% error rate on the MNIST task.

[1]  Seiichi Uchida,et al.  Handwritten character recognition using elastic matching based on a class-dependent deformation model , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[2]  Jitendra Malik,et al.  Shape Context: A New Descriptor for Shape Matching and Object Recognition , 2000, NIPS.

[3]  Oscar E. Agazzi,et al.  Keyword Spotting in Poorly Printed Documents using Pseudo 2-D Hidden Markov Models , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[5]  Seiichi Uchida,et al.  Eigen-deformations for elastic matching based handwritten character recognition , 2003, Pattern Recognit..

[6]  Bernhard Schölkopf,et al.  Training Invariant Support Vector Machines , 2002, Machine Learning.

[7]  Patrice Y. Simard,et al.  Best practices for convolutional neural networks applied to visual document analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[8]  Hermann Ney,et al.  Classification of Medical Images using Non-linear Distortion Models , 2004, Bildverarbeitung für die Medizin.

[9]  김대진.  A Numeral Character Recognition Using the PCA Mixture Model , 2000 .

[10]  Geoffrey E. Hinton,et al.  Modeling the manifolds of images of handwritten digits , 1997, IEEE Trans. Neural Networks.

[11]  Seiichi Uchida,et al.  A monotonic and continuous two-dimensional warping based on dynamic programming , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[12]  Daniel Keysers,et al.  Elastic image matching is NP-complete , 2003, Pattern Recognit. Lett..

[13]  Hermann Ney,et al.  Combination of Tangent Vectors and Local Representations for Handwritten Digit Recognition , 2002, SSPR/SPR.

[14]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[15]  Hermann Ney,et al.  Experiments with an extended tangent distance , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.