Features at different resolutions, from coarse to fine-grained, are implemented by means of a recursive classification scheme. 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.
Experimental results support the methodology presented.
The comparsion uses the same feature set all methods. This is at par with the other two traditional methods. IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol. Inc. All rights reserved.