Zoning and metaclasses for character recognition

Freitas, Cinthia Obladen de Almendra; Oliveira, Luiz S.; Aires, Simone B. K.; Bortolozzi, Flávio

doi:10.1145/1244002.1244146

Cited by 10 publications

(10 citation statements)

References 8 publications

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“…Distance was calculated considering groups of 2 classifiers. Initially, the experiments are based on groups of 2 classifiers trying to make more understandable the confusions occurred by metaclasses of characters [2].…”

Section: B Resultsmentioning

confidence: 99%

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Distance-based Disagreement Classifiers Combination

Freitas

Carvalho

Oliveira

et al. 2007

2007 International Joint Conference on Neural Networks

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We present a methodology to analyze Multiple Classifiers Systems (MCS) performance, using the diversity concept. The goal is to define an alternative approach to the conventional recognition rate criterion, which usually requires an exhaustive combination search. This approach defines a Distance-based Disagreement (DbD) measure using an Euclidean distance computed between confusion matrices and a soft-correlation rule to indicate the most likely candidates to the best classifiers ensemble. As case study, we apply this strategy to two different handwritten recognition systems. Experimental results indicate that the method proposed can be used as a low-cost alternative to conventional approaches.

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Section: B Resultsmentioning

confidence: 99%

“…Each image zone defines one classifier and these classifiers combined constitute a MCS. The zoning approach is beyond the scope of this paper [2]. IV.…”

Section: Hypothesis: Soft-correlation Rulementioning

confidence: 99%

Distance-based Disagreement Classifiers Combination

Freitas

Carvalho

Oliveira

et al. 2007

2007 International Joint Conference on Neural Networks

Self Cite

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“…In this approach each character is divided in a rectangle with Z parts, where Z = 2LR (L=left, R=right), 2UD (U=up, D=down), 4, and 6 zones. In Freitas et al [5] the zoning approach was investigated using the zones Z = 4, 5H, 5V and 7, as presented in the Figure 1.…”

Section: The Zoning Mechanismmentioning

confidence: 99%

“…After the metaclasses formation, we have used a zoning mapping approach proposed by Freitas et al [5] to differentiate characters at each cluster. For each character zoning 118 structural and directional features were extracted.…”

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confidence: 99%

Metaclasses and zoning for handwritten document recognition

Macario¹,

Silva²,

Souza

et al. 2013

The 2013 International Joint Conference on Neural Networks (IJCNN)

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This work presents a complete method for improving the handwritten document recognition. In this task some characters are confused with others because of their visual/structural similarity. A SOM and TreeSOM neural network were used to sort different characters in metaclasses. In each metaclass a zoning approach was applied trying to get particular features to improve the character classification. The experiments with this new approach were performed in the NIST database with the classic MLP and a fast neural network RBF-DDA. I. INTRODUCTIONHE document recognition is an important intelligent systems research area. Commercially, several companies use this technology to solve complex real world activities. Character recognition systems are increasingly powerful for printed characters, however much remains to be improved in the handwritten recognition task.Analyzing the automatic handwritten recognition problem the major issue is the visual/structural similarities among some characters, e.g. the letters "I" and "J". The use of metaclass is a recognition approach to deals with this type of problem [1]. The metaclass approach builds clusters with similar characters and considers different characteristics using a local strategy to recognize each cluster of characters.The human strategy to character recognition task is similar to the metaclass approach, we first associates a well known part of the character and then a specialized recognition is used [2]. From this assumption many works have been proposed with a local processing strategy. The use of parts of the characters to extract some local features is called zoning. Some authors proposed empirical zoning [2] [3] and others an automatic zoning [4].In this paper we propose a new approach to build metaclasses. The metaclasses were created by SOM neural network [6]. The SOM technique allows the creation of clusters containing elements with similar characteristics. To find the best SOM cluster map we used the evaluation technique treeSOM [7]. So the clusters were built according to the best possible cluster composition.V. Macário is with the

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“…Authors in [24] use 8 x 8 symmetrical zones where the percentage of black pixels in each zone is calculated while in [25], the total number of black pixels is calculated separately for each line in the horizontal and vertical direction for each zone. Authors in [26] and [27] develop a non-symmetrical zoning, with very good results.…”

Section: Zoningmentioning

confidence: 99%

Document zone content classification for technical document images using Artificial Neural Networks and Support Vector Machines

İbrahim¹,

Isa

Rajkumar

et al. 2009

2009 Second International Conference on the Applications of Digital Information and Web Technologies

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Artificial Neural Networks (ANN) are a classic pattern classifier and widely applicable to various problems and are relatively easy to use. Three of the most popular ANNs are Multilayer Perceptron (MLP) with Backpropagation learning algorithm, Self Organizing Map (SOM) and Recurrent Neural Network (RNN). Support Vector Machines (SVM) have gained great interest in the last few years in pattern recognition. Thus, this research compares the recognition performance of text and non-text images (text, table, figure and graph) from technical document images based on the pixel intensity of various zones between BPNN, SOM, RNN and SVM. Symmetrical and non-symmetrical zoning algorithms were compared as input. 400 different datasets have been tested and the experiments indicate that SVM classification is superior to the other three classifiers. The experiments also indicate that the combination of symmetrical and non-symmetrical zoning design is better than non-symmetrical or symmetrical zoning only.

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Zoning and metaclasses for character recognition

Cited by 10 publications

References 8 publications

Distance-based Disagreement Classifiers Combination

Distance-based Disagreement Classifiers Combination

Metaclasses and zoning for handwritten document recognition

Document zone content classification for technical document images using Artificial Neural Networks and Support Vector Machines

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