A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using consensus sequence voting to correct OCR errors
Computer Vision and Image Understanding
A technique for computer detection and correction of spelling errors
Communications of the ACM
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Multiple Classifier Combination Methodologies for Different Output Levels
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new generation of textual corpora: mining corpora from very large collections
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
"Inside the bible": segmentation, annotation and retrieval for a new browsing experience
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Improving OCR accuracy for classical critical editions
ECDL'09 Proceedings of the 13th European conference on Research and advanced technology for digital libraries
Why multiple document image binarizations improve OCR
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
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In spite of the improvement of CommercialOptical Character Recognition (OCR) during the last years, their ability to process different kinds of documents can also be a default. They cannot produce a perfect recognition for all documents. However they allow producing high result for standard cases. We propose in this paper a model combining several OCRs and a specialized ICR (Intelligent Character Recognition) based on a convolutional neural network to complement them. Instead of just performing several OCRs in parallel and applying a fusing rule of the results, a specialized neural network with an adaptive topology is added to complement the OCRs in function of the OCRs errors. This system has been tested on ancient documents containing old characters and old fonts not used in contemporary documents. The OCRs combination increases the recognition of about 3% whereas the ICR improves the recognition of rejected characters of more than 5%.