Recognition of Cursive Roman Handwriting - Past, Present and Future
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Hierarchical approximate matching for retrieval of chinese historical calligraphy character
Journal of Computer Science and Technology
Design and evaluation of an adaptive combination framework for OCR result strings
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Ensemble methods to improve the performance of an English handwritten text line recognizer
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
Improving isolated handwritten word recognition using a specialized classifier for short words
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
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Because of large shape variations in human handwriting, recognition accuracy of cursive handwritten word is hardly satisfying using a single classifier. In this paper we introduce a framework to combine results of multiple classifiers and present an intuitive run-time weighted opinion pool (RWOP) combination approach for recognizing cursive handwritten words with a large size vocabulary. The individual classifiers are evaluated run-time dynamically. The final combination is weighted according to their local performance. For an open vocabulary recognition task, we use the ROVER algorithm to combine the different strings of characters provided by each classifier. Experimental results for recognizing cursive handwritten words demonstrate that our new approach achieves better recognition performance and reduces the relative error rate significantly.