The nature of statistical learning theory
The nature of statistical learning theory
Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Learnability and Design of Output Codes for Multiclass Problems
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Multi-Experts for Touching Digit String Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data driven approach to designing minimum hamming distance polychotomizer
Proceedings of the 2005 ACM symposium on Applied computing
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
New results on error correcting output codes of kernel machines
IEEE Transactions on Neural Networks
Data-driven decomposition for multi-class classification
Pattern Recognition
An incremental node embedding technique for error correcting output codes
Pattern Recognition
Separability of ternary codes for sparse designs of error-correcting output codes
Pattern Recognition Letters
Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes
Journal of Signal Processing Systems
Optimizing linear discriminant error correcting output codes using particle swarm optimization
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Two stage reject rule for ECOC classification systems
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Design of reject rules for ECOC classification systems
Pattern Recognition
Adaptive error-correcting output codes
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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This paper describes a new approach to recognize touching numeral strings. Currently most methods for numeral string recognition require segmenting the string image into separate numerals. As a result, the recognition system heavily depends on the reliability of the segmentation module. This study explores the holistic strategy directly on the string images without segmentation. It builds the novel classifier by combining binary classi- fiers based on Data-driven Error Correcting Output Coding (DECOC). The dimensions of input images are reduced using principal components analysis. Support vector machines are used as base learners. Experiments on NIST SD19 touching numeral pairs confirm that DECOC can achieve favorable performance compared with other multi-class holistic classifiers. The method provides the flexibility of controlling the computational complexity versus accuracy. We also discuss an implementation suitable for distributing computing by decomposing the ensemble into subtasks.