Improved directional morphological operations for separation of characters from maps/graphics

Luo, Huizhu; Kasturi, Rangachar

doi:10.1007/3-540-64381-8_37

Cited by 21 publications

(13 citation statements)

References 6 publications

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“…Separating text and graphics in map images is well studied [2], [3], and several works follow this separation by robust character recognition [4], [5], [6], [7], [8]. Most research focuses on modern, topographic, or cadaster maps, rather than historical maps.…”

Section: A Related Workmentioning

confidence: 99%

“…3 For the contiguous lower forty-eight states, the gazetteer of the USBGN Geographic Names Information System (GNIS) contains slightly over two million entries (called "features") in sixty five categories from Airport to Woods, though we use only Civil and Populated Place, which constitute the majority of our corpus toponyms. Each feature has primary geographical coordinates and is indexed by its state and county.…”

Section: Data 1) Gazetteermentioning

confidence: 99%

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Toponym Recognition in Historical Maps by Gazetteer Alignment

Weinman

2013

2013 12th International Conference on Document Analysis and Recognition

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Abstract-Historical map documents are increasingly digitized for widespread access, but most are only coarsely indexed with meta-data while the contents are largely unsearchable. We propose to increase searchability by automatically recognizing the place names in these digitized artifacts. Using a word recognition system that produces a noisy ranked list of initial hypotheses from a lexicon of viable toponyms, we form a joint probabilistic model for inferring the most likely latent alignment between image toponyms and a gazetteer of known place locations. After a robust generalized RANSAC algorithm identifies the global alignment, we rerank the toponym hypotheses by their posterior probability. Experiments demonstrate a significant boost in word recognition accuracy on a manually annotated set of 19th century U.S. state and regional maps.

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Section: A Related Workmentioning

confidence: 99%

Section: Data 1) Gazetteermentioning

confidence: 99%

Toponym Recognition in Historical Maps by Gazetteer Alignment

Weinman

2013

2013 12th International Conference on Document Analysis and Recognition

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“…Some other works utilize different separation like Luo et al [8] who try to separate roads and text labels in simCopyright c 2012 The Institute of Electronics, Information and Communication Engineers ple maps. Or Hai et al in [9] who separate pixels into three possible groups: road, non-road and noise regions.…”

Section: Related Workmentioning

confidence: 99%

Automatic Road Area Extraction from Printed Maps Based on Linear Feature Detection

Callier

Saitō

2012

IEICE Trans. Inf. & Syst.

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SUMMARYRaster maps are widely available in the everyday life, and can contain a huge amount of information of any kind using labels, pictograms, or color code e.g. However, it is not an easy task to extract roads from those maps due to those overlapping features. In this paper, we focus on an automated method to extract roads by using linear features detection to search for seed points having a high probability to belong to roads. Those linear features are lines of pixels of homogenous color in each direction around each pixel. After that, the seeds are then expanded before choosing to keep or to discard the extracted element. Because this method is not mainly based on color segmentation, it is also suitable for handwritten maps for example. The experimental results demonstrate that in most cases our method gives results similar to usual methods without needing any previous data or user input, but do need some knowledge on the target maps; and does work with handwritten maps if drawn following some basic rules whereas usual methods fail.

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“…Wenyin et al [7] recognized horizontal or vertical text. Luo et al [3] detached characters from straight lines. For maps, the problem is much more complex since the touching or overlapping as well as many other character configurations are commonly presented in maps.…”

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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Improved directional morphological operations for separation of characters from maps/graphics

Cited by 21 publications

References 6 publications

Toponym Recognition in Historical Maps by Gazetteer Alignment

Toponym Recognition in Historical Maps by Gazetteer Alignment

Automatic Road Area Extraction from Printed Maps Based on Linear Feature Detection

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

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