Writer Adaptation for Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems
OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems
Optical Character Recognition for Cursive Handwriting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous Approach to Segmentation of Handwritten Text
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Probabilistic Model for Segmentation Based Word Recognition with Lexicon
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
SegGen: a genetic algorithm for linear text segmentation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Similarity-based training set acquisition for continuous handwriting recognition
Information Sciences: an International Journal
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In this paper, a method of semi-automatic training set acquisition for character classifiers used in cursive handwriting recognition is described. The training set consists of character samples extracted from a training corpus by segmentation. The method first splits the word images from the corpus into a sequence of graphemes. Then, the set of candidate segmentation variants is elicited with an evolutionary algorithm, where the segmentation variant determines subdivision of grapheme sequences of words into subsequences corresponding to consecutive letters. Segmentation variants are modeled by a chromosome population. Next, each segmentation variant from the final population is tuned in an iterative process and the best chromosome is selected. Then character samples resulting from application of the segmentation modeled by the selected chromosome are grouped into sets corresponding to letters from the alphabet. Finally, the most outstanding samples are rejected so as to maximize the accuracy of words recognition obtained with a character classifier trained with the reduced samples set.