Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Weighted central moments in pattern recognition
Pattern Recognition Letters
Writer Adaptation for Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Optical Character Recognition for Cursive Handwriting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Noise and Background Removal from Handwriting Images
IIS '97 Proceedings of the 1997 IASTED International Conference on Intelligent Information Systems (IIS '97)
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
Machine Printed Text and Handwriting Identification in Noisy Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text line segmentation of historical documents: a survey
International Journal on Document Analysis and Recognition
CISIM '07 Proceedings of the 6th International Conference on Computer Information Systems and Industrial Management Applications
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Script-Independent Text Line Segmentation in Freestyle Handwritten Documents
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hierarchical approach to recognition of handwritten Bangla characters
Pattern Recognition
Preprocessing of Low-Quality Handwritten Documents Using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text line and word segmentation of handwritten documents
Pattern Recognition
SegGen: a genetic algorithm for linear text segmentation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Markov models for offline handwriting recognition: a survey
International Journal on Document Analysis and Recognition
Binary segmentation with neural validation for cursive handwriting recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Historical document enhancement using LUT classification
International Journal on Document Analysis and Recognition
Semi-automatic training sets acquisition for handwriting recognition
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Evolutionary algorithms for optimization problems with uncertainties and hybrid indices
Information Sciences: an International Journal
High performance classifiers combination for handwritten digit recognition
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Binary segmentation algorithm for English cursive handwriting recognition
Pattern Recognition
Information Sciences: an International Journal
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In the paper we consider the problem of continuous handwriting segmentation into individual characters. The ultimate aim is to create the set of isolated character images used as a training set for the writer-dependent handwriting recognizer. Analytic approach is applied, where word recognition is based on the individual classification of characters. The input to the proposed segmentation method is a handwritten text image consisting of known words. The method consists of three stages. Initially, images of isolated words are over-segmented into sequences of graphemes. At the first stage the genetic algorithm is used to create the set of segmentation variants that are likely to correspond to actual characters. The fitness function is based on the similarity of images within subsets of images of the same character. At the second stage, the set of segmentation variants elicited as the last generation of the genetic algorithm is refined by applying a sequence of subtle segment boundary displacements that increase the similarity of images within sets of the same characters. In the third stage the most typical character prototypes are selected and fixed in word images. The segmentation of remaining words fragments is achieved by maximizing the similarity to the fixed character prototypes. The accuracy of handwritten text recognition with the acquired character images after each stage was experimentally evaluated. Experiments with continuous handwriting recognition show that application of each stage improves the word recognition accuracy.