Orthogonal Moment Features for Use With Parametric and Non-Parametric Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Off-Line, Handwritten Numeral Recognition by Perturbation Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Processing of Line-Drawing Images
ACM Computing Surveys (CSUR)
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multilingual, multimodal digital video library system
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
An Adaptive Approach to Offline Handwritten Word Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active Handwritten Character Recognition Using Genetic Programming
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Utilization of Hierarchical, Stochastic Relationship Modeling for Hangul Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A generalized metric distance between hierarchically partitioned images
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
A hierarchical approach to recognition of handwritten Bangla characters
Pattern Recognition
Handwritten character recognition through two-stage foreground sub-sampling
Pattern Recognition
Zoning methods for handwritten character recognition: A survey
Pattern Recognition
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This paper describes a character recognition methodology (henceforth referred to as Hierarchical OCR) that achieves high speed and accuracy by using a multiresolution and hierarchical feature space. Features at different resolutions, from coarse to fine-grained, are implemented by means of a recursive classification scheme. Typically, recognizers have to balance the use of features at many resolutions (which yields a high accuracy), with the burden on computational resources in terms of storage space and processing time. We present in this paper, a method that adaptively determines the degree of resolution necessary in order to classify an input pattern. This leads to optimal use of computational resources. The Hierarchical OCR dynamically adapts to factors such as the quality of the input pattern, its intrinsic similarities and differences from patterns of other classes it is being compared against, and the processing time available. Furthermore, the finer resolution is accorded to only certain 驴zones驴 of the input pattern which are deemed important given the classes that are being discriminated. Experimental results support the methodology presented. When tested on standard NIST data sets, the Hierarchical OCR proves to be 300 times faster than a traditional K-nearest-neighbor classification method, and 10 times faster than a neural network method. The comparsion uses the same feature set for all methods. Recognition rate of about 96 percent is achieved by the Hierarchical OCR. This is at par with the other two traditional methods.