graphs serve as highly powerful representational tools which are particularly well-suited for a great variety of computer applications such as computer vision and handwriting recognition. In this work, we set forward ...
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graphs serve as highly powerful representational tools which are particularly well-suited for a great variety of computer applications such as computer vision and handwriting recognition. In this work, we set forward a novel fuzzygraph-based framework to model each script as a fuzzyattributedgraph (FARG) and reformulate the task of classification as a fuzzygraph matching problem. In this respect, we start by modeling each script as a FARG where a segment represents a node and edges represent relations between segments. Hence, each node and edge is characterized by a set of fuzzy membership degrees describing their properties. The use of fuzzy attributes allows us to guarantee more robustness against uncertainty, ambiguity and vagueness. We model then the classification problem as a Multi-Criteria Decision Making (MCDM) problem. Given a pair of graphs as input, a similarity score between them is computed by reasoning on the pair through a tree-search based optimal matching algorithm. A full analysis is performed on two large datasets: MAYASTROUN and ADAB. Our approach achieved a 0.69% character error rate (CER) and a 1.45% word error rate (WER) against the MAYASTROUN data set and a 2.66% WER against the ADAB data set. Afterwards, we compare the results and the functionality that we obtain with the findings from existing state of the art approaches. In this regard, the accuracy and goodness of fit of the presented work is conclusively validated.
In this research, we attempt to propose a novel graph-based approach for online handwritten character recognition. Unlike the most well-known online handwritten recognition methods, which are based on statistical repr...
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ISBN:
(纸本)9789897584022
In this research, we attempt to propose a novel graph-based approach for online handwritten character recognition. Unlike the most well-known online handwritten recognition methods, which are based on statistical representations, we set forward a new approach based on structural representation to overcome the inherent deformations of handwritten characters. An attributedrelationalgraph (ARG) is dedicated to allowing the direct labeling of nodes (strokes) and edges (relationships) of a graph to model the input character. Each node is characterized by a set of fuzzy membership degrees describing their properties (type, size). fuzzy description is invested in order to guarantee more robustness against uncertainty, ambiguity and vagueness. ARGs edges stand for spatial relationships between different strokes. At a subsequent stage, a tree-search based optimal matching algorithm is explored, which allows the search for character structures i.e the minimum cost of nodes. Experiments performed on ADAB and IRONOFF datasets, reveal promising results. In particular, the comparison with the state of the art demonstrates the significance of the proposed system.
fuzzy median graph is an important new concept that can represent a set of fuzzygraphs by a representative fuzzygraph prototype. However, the computation of a fuzzy median graph remains a computationally expensive t...
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fuzzy median graph is an important new concept that can represent a set of fuzzygraphs by a representative fuzzygraph prototype. However, the computation of a fuzzy median graph remains a computationally expensive task. In this paper, we propose a new approximate algorithm for the computation of the fuzzy Generalized Median graph (FGMG) based on fuzzy attributed relational graph (FARG) embedding in a suitable vector space in order to capture the maximum information in graphs and to improve the accuracy and speed of document image retrieval processing. In this study, we focus on the application of FGMGs to the Content-based Document Retrieval (CBDR) problem. Experiments on real and synthetic databases containing a large number of FARGs with large sizes show that a CBDR using the FGMG as a dataset representative yields better results than an exhaustive and sequential retrieval in terms of gains in accuracy and time processing. (C) 2017 Elsevier Ltd. All rights reserved.
This paper presents a novel method for on-line handwritten Chinese character recognition. In our method, each category of character is described by a fuzzy attributed relational graph (FARG). A relaxation algorithm is...
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ISBN:
(纸本)0818678984;0818678992
This paper presents a novel method for on-line handwritten Chinese character recognition. In our method, each category of character is described by a fuzzy attributed relational graph (FARG). A relaxation algorithm is developed to match the input pattern with every FARG. For decision making, a similarity measure is established via statistical technique to calculate the matching degree between the input pattern and referenced FARG, according to which the recognition result is determined. The principle of our method make it very robust against stroke connection and stroke order variation as well as stroke shape deformation. A database of 22530 samples collected from 6 subjects is used to test our recognition system which can recognize 3755 categories of Chinese character. The result shows that our method is very effective: a top-1 recognition rate of 98.8% and a top-10 of 99.7% are reached.
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