A Prototype for adaptive association of street names with streets on maps

Nagy, George; Samal, Ashok; Seth, Sharad; Fisher, Thomas J.; Guthmann, E.; Kalafala, K.; Li, L.; Sarkar, Prateek; Sivasubramaniam, Sumathi; Xu, Yong

doi:10.1007/3-540-64381-8_58

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…These kind of approach try to extract the set of vectors corresponding to potential characters or symbols [Shimotsuji 1994], or try to detect sets of vectors which do not match with the line model of the document [Nagy 1998]. Boatto [Boatto 1992b] proposed to detect the connected characters by using a technique based on "run-length" analysis.…”

Section: Connected Characters Managementmentioning

confidence: 99%

Symbol and character recognition: application to engineering drawings

Adam

Ogier

Cariou³

et al. 2000

International Journal on Document Analysis and Recognition

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In this paper, we consider the general problem of technical document interpretation, applied to the documents of the French Telephonic Operator, France Telecom. More precisely, we focus the content of this paper on the computation of a new set of features allowing the classification of multi-oriented and multi-scaled patterns. This set of Invariant is based on the Fourier Mellin Transform. The interests of this computation rely on the excellent classification rate which is obtained with this method, and also on the possibility to use this Fourier Mellin transform within a "filtering mode", that permits to solve the well known difficult problem of connected character recognition.

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Section: Connected Characters Managementmentioning

confidence: 99%

Symbol and character recognition: application to engineering drawings

Adam

Ogier

Cariou³

et al. 2000

International Journal on Document Analysis and Recognition

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show abstract

“…It is pondered and hierarchically built. To retrieve street names Nagy et al [10] used one of the four black layers. Taking the hue component from a HSV model for segmentation, they subtracted the street layer from the black layer and then made a connected component analysis to distinguish text characters.…”

Section: Introductionmentioning

confidence: 99%

Text/Graphics Separation and Recognition in Raster-Scanned Color Cartographic Maps

Velázquez¹,

Levachkine

2004

Graphics Recognition. Recent Advances and Perspectives

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Abstract.A method to separate and recognize the touching/overlapping alphanumeric characters is proposed. The characters are processed in raster-scanned color cartographic maps. The map is segmented first to extract all text strings including those that are touching other symbols, strokes and characters. Second, OCR-based recognition with Artificial Neural Networks (ANN) is applied to define the coordinates, size and orientation of alphanumeric character strings in each case presented in the map. Third, four straight lines or a number of "curves" computed as a function of primarily recognized by ANN characters are extrapolated to separate those symbols that are attached. Finally, the separated characters input into ANN again to be finally identified. Results showed high method's rendering in the context of raster-to-vector conversion of color cartographic images.

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Research advances in graphics recognition: An update

Kasturi

Luo

1997

Advances in Document Image Analysis

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A Prototype for adaptive association of street names with streets on maps

Cited by 5 publications

References 8 publications

Symbol and character recognition: application to engineering drawings

Symbol and character recognition: application to engineering drawings

Text/Graphics Separation and Recognition in Raster-Scanned Color Cartographic Maps

Research advances in graphics recognition: An update

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