Semantic Analysis and Recognition of Raster-Scanned Color Cartographic Images

Levachkine, Serguei; Velázquez, Aurelio; Alexandrov, V. V.; Kharinov, Mikhail V.

doi:10.1007/3-540-45868-9_15

Cited by 24 publications

(32 citation statements)

References 6 publications

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“…For example, Mexico has full-territory coverage by recognized topographic maps in scale 1:50,000, but not in 1:25,000; this is a commonality for many countries [4]. Moreover, the C2FS method is in essence a simultaneous segmentation-recognition system [5] [7].…”

Section: Resultsmentioning

confidence: 99%

“…To date, it is hard to see the ways to obtain even a partial, but general, i.e. applied to any type of raster objects, solution of the problem; see survey papers [2][4] [7] for detail discussion. In certain sense, the C2FS method represents a promising alternative.…”

Section: Introduction Formal Statement Of the Problemmentioning

confidence: 99%

“…of the vector objects from ω. After, the objects of Ω have being vectored we can vector the objects from Θ by one of the recognition modules [2] [7] as a "new" cartographic material.…”

Section: Introduction Formal Statement Of the Problemmentioning

confidence: 99%

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Color Cartographic Pattern Recognition Using the Coarse to Fine Scale Method

González-Gómez

Levachkine

2004

Lecture Notes in Computer Science

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Abstract. Hard problem of cartographic pattern recognition in fine scale 1 maps, using information that comes from coarse scale 2 maps, is considered. The maps are raster-scanned color maps of different thematic, representing the same territory in coarse and fine scale respectively. A solution called Coarse-to-Fine Scale Method is proposed. This method is defined in terms of means: coarse scale maps and their information; concepts: image associated function, cartographic knowledge domain and cartographic pattern; and tools: a set of clustering criteria of the Logical Combinatorial Pattern Recognition.

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Section: Resultsmentioning

confidence: 99%

Section: Introduction Formal Statement Of the Problemmentioning

confidence: 99%

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Color Cartographic Pattern Recognition Using the Coarse to Fine Scale Method

González-Gómez

Levachkine

2004

Lecture Notes in Computer Science

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“…Text and graphics are often overlapping that makes separation of the elements difficult, Cao and Tan (2002) have proposed a method for text recognition in maps. Based on the observation that character strokes generally consist of short segments as compared to cartographic elements, Levachkine et al (2002) used a combination of line extension and line width to separate elements and improve text extraction. Text can be also detected using color attributes when the color of text characters differs from other objects.…”

Section: Related Workmentioning

confidence: 99%

Automatic Text Removal and Replacement in Scanned Meteorological Thematic Maps

Abdullah¹,

Alshamat²,

Bakhashwain³

et al. 2015

Journal of Computer Science

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Geo-referenced data is required in many Geographic Information System (GIS) applications and one source of such data being maps. Meteorological maps are color-coded with different regions corresponding to different values of a parameter, parsing the image to convert into data is not very difficult. However, text and different planimetric elements overlaid on the regions in the map makes accurate image to data conversion a challenging problem, because it is almost impossible to exactly replace what was underneath the text or icons i.e., the need for inpainting. In this study, we propose a probabilistic technique that uses the probability of occurrence of colors present in the map along with the occurrence of those colors in the spatial neighborhood of the pixel (corresponding to text) whose color is required to be replaced. We test the limits of our proposed technique using simulated data and compare results with a popular image editing tool using public domain data with promising results.

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“…However, they do not recognize characters. In [5], Levachkine et al used false colors in a RGB model. They applied different combinations of basic colors to segment map objects, and then a neighborhood analysis to recover or eliminate pixels.…”

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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Semantic Analysis and Recognition of Raster-Scanned Color Cartographic Images

Cited by 24 publications

References 6 publications

Color Cartographic Pattern Recognition Using the Coarse to Fine Scale Method

Color Cartographic Pattern Recognition Using the Coarse to Fine Scale Method

Automatic Text Removal and Replacement in Scanned Meteorological Thematic Maps

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

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