HANDWRITTEN WORD RECOGNITION USING CLASSIFIER ENSEMBLES GENERATED FROM MULTIPLE PROTOTYPES

2004 ◽  
Vol 18 (05) ◽  
pp. 957-974 ◽  
Author(s):  
SIMON GÜNTER ◽  
HORST BUNKE
Keyword(s):  
Handwriting Recognition ◽  
Recognition Rate ◽  
Classifier Ensembles ◽  
Multiple Classifier Systems ◽  
Classification Problems ◽  
Classifier Systems ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Multiple Classifier ◽  
System 1

Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. In this paper, we describe our efforts towards improving the performance of state-of-the-art handwriting recognition systems through the use of classifier ensembles. There are many examples of classification problems in the literature where multiple classifier systems increase the performance over single classifiers. Normally one of the two following approaches is used to create a multiple classifier system. (1) Several classifiers are developed completely independent of each other and combined in a last step. (2) Several classifiers are created out of one prototype classifier by using so-called classifier ensemble creation methods. In this paper an algorithm which combines both approaches is introduced and it is used to increase the recognition rate of a hidden Markov model (HMM) based handwritten word recognizer.

INTEGRATION OF n-GRAM LANGUAGE MODELS IN MULTIPLE CLASSIFIER SYSTEMS FOR OFFLINE HANDWRITTEN TEXT LINE RECOGNITION

2008 ◽  
Vol 22 (07) ◽  
pp. 1301-1321 ◽  
Author(s):  
ROMAN BERTOLAMI ◽  
HORST BUNKE
Keyword(s):  
Language Model ◽  
Language Models ◽  
Combination Method ◽  
Text Line ◽  
Multiple Classifier Systems ◽  
Classifier Systems ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Multiple Classifier ◽  
N Gram

Current multiple classifier systems for unconstrained handwritten text recognition do not provide a straightforward way to utilize language model information. In this paper, we describe a generic method to integrate a statistical n-gram language model into the combination of multiple offline handwritten text line recognizers. The proposed method first builds a word transition network and then rescores this network with an n-gram language model. Experimental evaluation conducted on a large dataset of offline handwritten text lines shows that the proposed approach improves the recognition accuracy over a reference system as well as over the original combination method that does not include a language model.

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.

RECOGNITION OF HANDWRITTEN DEVANAGARI SCRIPT

2010 ◽  
Vol 24 (05) ◽  
pp. 809-822 ◽  
Author(s):  
LATESH MALIK ◽  
P. S. DESHPANDE
Keyword(s):  
Character Recognition ◽  
Template Matching ◽  
Recognition Rate ◽  
Horizontal Line ◽  
Word Level ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Multiple Classifier ◽  
Novel Concept ◽  
Combined Classifier

Segmentation of handwritten text into lines, words and characters is one of the important steps in the handwritten text recognition process. In this paper, we propose a float fill algorithm for segmentation of unconstrained Devanagari text into words. Here, a text image is directly segmented into individual words. Rectangular boundaries are drawn around the words and horizontal lines are detected with template matching. A mask is designed for detecting the horizontal line and is applied to each word from left to right and top to bottom of the document. Header lines are removed for character separation. A new segment code features are extracted for each character. In this paper, we present the results of multiple classifier combination for offline handwritten Devanagari characters. The use of regular expressions in handwritten characters is a novel concept and they are defined in a manner so that they can become more robust to noise. We have achieved an accuracy of 94% for word level segmentation, 95% for coarse classification and 85% for fine classification of character recognition. On experimentation with a dataset of 5000 samples of characters, the overall recognition rate observed is 95% as we considered top five choice results. The proposed combined classifier can be applied to handwritten character recognition of any other language like English, Chinese, Arabic, etc. and can recognize the characters with same accuracy.18 For printed characters we have achieved accuracy of 100%, only by applying the regular expression classifier.17

Decision Tree-Based Multiple Classifier Systems: An FPGA Perspective

2015 ◽  
pp. 194-205 ◽  
Author(s):  
Mario Barbareschi ◽  
Salvatore Del Prete ◽  
Francesco Gargiulo ◽  
Antonino Mazzeo ◽  
Carlo Sansone
Keyword(s):  
Decision Tree ◽  
Multiple Classifier Systems ◽  
Classifier Systems ◽  
Multiple Classifier
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