scholarly journals Optimization of Weights in a Multiple Classifier Handwritten Word Recognition System Using a Genetic Algorithm

2004 ◽  
Vol 3 (1) ◽  
pp. 25 ◽  
Author(s):  
Simon Guenter ◽  
Horst Bunke
Keyword(s):  
Genetic Algorithm ◽  
Word Recognition ◽  
Recognition System ◽  
Multiple Classifier ◽  
Handwritten Word Recognition

Online Handwritten Gujarati Word Recognition

2019 ◽  
Vol 9 (1) ◽  
pp. 35-50 ◽  
Author(s):  
Vishal A. Naik ◽  
Apurva A. Desai
Keyword(s):  
Word Recognition ◽  
Recognition System ◽  
Support Vector ◽  
Post Processing ◽  
Mapping Rule ◽  
Hybrid Features ◽  
Rule Based ◽  
Chain Code ◽  
Handwritten Word Recognition ◽  
Gujarati Language

In this article, an online handwritten word recognition system for the Gujarati language is presented by combining strokes, characters, punctuation marks, and diacritics. The authors have used a support vector machine classification algorithm with a radial basis function kernel. The authors used a hybrid features set. The hybrid feature set consists of directional features with curvature data. The authors have used a normalized chain code and zoning-based chain code features. Words are a combination of characters and diacritics. Recognized strokes require post-processing to form a word. The authors have used location-based and mapping rule-based post-processing methods. The authors have achieved an accuracy of 95.3% for individual characters, 91.5% for individual words, and 83.3% for sentences. The average processing time for individual characters is 0.071 seconds.

MULTIPLE CLASSIFIER SYSTEMS IN OFFLINE HANDWRITTEN WORD RECOGNITION — ON THE INFLUENCE OF TRAINING SET AND VOCABULARY SIZE

2004 ◽  
Vol 18 (07) ◽  
pp. 1303-1320 ◽  
Author(s):  
SIMON GÜNTER ◽  
HORST BUNKE
Keyword(s):  
Word Recognition ◽  
Recognition Performance ◽  
Ensemble Methods ◽  
Multiple Classifier Systems ◽  
Classifier Systems ◽  
Vocabulary Size ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Multiple Classifier ◽  
Handwritten Word Recognition

Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. Recently, a number of classifier creation methods, known as ensemble methods, have been proposed in the field of machine learning. It has been shown that these methods are able to substantially improve recognition performance in complex classification tasks. In this paper we examine the influence of the vocabulary size and the number of training samples on the performance of three ensemble methods in the context of handwritten word recognition. The experiments were conducted with two different offline hidden Markov model based handwritten word recognizers.

An Off-Line Cursive Handwritten Word Recognition System and Its Application to Legal Amount Interpretation

1997 ◽  
Vol 11 (05) ◽  
pp. 757-770 ◽  
Author(s):  
Ke Han ◽  
Ishwar K. Sethi
Keyword(s):  
Word Recognition ◽  
Recognition System ◽  
Great Accuracy ◽  
Input Word ◽  
Postal Service ◽  
Indexing Scheme ◽  
Cursive Script ◽  
System Maps ◽  
Handwritten Word Recognition ◽  
Verification Process

Off-line cursive script recognition has got increasing attention during the last three decades since it is of interest in several areas such as banking and postal service. An off-line cursive handwritten word recognition system is described in this paper and is used for legal amount interpretation in personal checks. The proposed recognition system uses a set of geometric and topologic features to characterize each word. By considering the spatial distribution of these features in a word image, the proposed system maps each word into two strings of finite symbols. A local associative indexing scheme is then used on these strings to organize a vocabulary. When presented with an unknown word, the system uses the same indexing scheme to retrieve a set of candidate words likely to match the input word. A verification process is then carried out to find the best match among the candidate set. The performance of the proposed system has been tested with a legal amount image database from real bankchecks. The results obtained indicate that the proposed system is able to recognize legal amounts with great accuracy.

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