Using consensus sequence voting to correct OCR errors
Computer Vision and Image Understanding
Beatrix: a self-learning system for off-line recognition of handwritten texts
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
The String-to-String Correction Problem
Journal of the ACM (JACM)
Proceedings of the First International Workshop on Multiple Classifier Systems
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Experiments with Classifier Combining Rules
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
A Full English Sentence Database for Off-Line Handwriting Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Handwritten Sentence Recognition
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Recognition of Cursive Roman Handwriting - Past, Present and Future
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Generalized median string computation by means of string embedding in vector spaces
Pattern Recognition Letters
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There are problems in pattern recognition where the output of a system is a sequence of classes rather than a single class. A well-known example is handwritten sentence recognition. In order to make those problems amenable to classifier combination techniques, an algorithm for sequence alignment must be provided. The present paper describes such an algorithm. The algorithm extends an earlier method by including information about the location of each pattern in a sequence. The proposed approach is evaluated in the context of a system for handwritten sentence recognition. It is demonstrated through experiments that by the use of positional information the computationally expensive process of multiple sequence alignment can be significantly sped up without loosing recognition accuracy.