Performance Characterization of Shape Descriptors for Symbol Representation

Valveny, Ernest; Tabbone, Salvatore; Ramos, Oriol; Philippot, Emilie

doi:10.1007/978-3-540-88188-9_26

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…Theses descriptors provide high performance for symbol recognition [26]. Table II shows the results obtained following the descriptors and the distances used.…”

Section: Resultsmentioning

confidence: 97%

On the Scalability of Graphic Symbol Recognition

Salmon

Wendling

2014

2014 22nd International Conference on Pattern Recognition

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This paper deals with a complex symbol recognition process considering a large number of classes and only one training image per class. Furthermore, the response times of recognition system should be short and the interpretation of results must be easy. In this particular case, both statistical and structural methods are not the most suitable. A new composite descriptor and a similarity measure are proposed. Experimental results show the proposed method outperforms two descriptors widely used in symbol recognition with industrial data.

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“…Theses descriptors provide high performance for symbol recognition [26]. Table II shows the results obtained following the descriptors and the distances used.…”

Section: Resultsmentioning

confidence: 97%

On the Scalability of Graphic Symbol Recognition

Salmon

Wendling

2014

2014 22nd International Conference on Pattern Recognition

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“…In the area of document image analysis most work has been performed for graphical symbol recognition/indexing. In this domain, a recent paper compared various shape descriptors for symbol representation [11]. An extension of the bag of visual words method based on Shape Context has been proposed in [9] for symbol retrieval.…”

Section: Indexing By Bag Of Visual Wordsmentioning

confidence: 99%

Mathematical Symbol Indexing Using Topologically Ordered Clusters of Shape Contexts

Marinai

Miotti

Soda

2009

2009 10th International Conference on Document Analysis and Recognition

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This paper addresses the indexing and retrieval of mathematical symbols from digitized documents. The proposed approach exploits Shape Contexts (SC) to describe the shape of mathematical symbols. Starting from the vector space method, that is based on SC clustering, we explore the use of topological ordered clusters to improve the retrieval performance. The clustering is computed by means of SelfOrganizing Maps that organize the clusters in two dimensional topologically ordered feature maps. The retrieval performance are compared with those obtained using the K-means clustering on a large collection of mathematical symbols gathered from the widely used INFTY database.

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“…Concerning symbol recognition a contribution using such tools has been proposed recently by [51]. The authors apply the measures of homogeneity, separability, recogni-tion rate and precision-recall to evaluate a collection of shape descriptors applied to the recognition of segmented symbol images.…”

Section: Performance Characterizationmentioning

confidence: 99%

Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview

Delalandre

Valveny

Lladós

2008

2008 the Eighth IAPR International Workshop on Document Analysis Systems

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This paper deals with the topic of performance evaluation of the symbol recognition & spotting systems. It presents an overview as a result of the work and the discussions undertaken by a working group on this subject. The paper starts by giving a general view of symbol recognition & spotting and performance evaluation. Next, the two main issues of performance evaluation are discussed: groundtruthing and performance characterization. Different problems related to both issues are addressed: groundtruthing of real documents, generation of synthetic documents, degradation models, the use of a priori knowledge, mapping of the groundtruth with the system results, and so on. Open problems arising from this overview are also discussed at the end of the paper.

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Performance Characterization of Shape Descriptors for Symbol Representation

Cited by 8 publications

References 12 publications

On the Scalability of Graphic Symbol Recognition

On the Scalability of Graphic Symbol Recognition

Mathematical Symbol Indexing Using Topologically Ordered Clusters of Shape Contexts

Performance Evaluation of Symbol Recognition and Spotting Systems: An Overview

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