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Text/Graphics Separation for Business Card Images for Mobile Devices
Separation of the text regions from background texture and graphics is a...
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Binarizing Business Card Images for Mobile Devices
Business card images are of multiple natures as these often contain grap...
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Segmentation of Camera Captured Business Card Images for Mobile Devices
Due to huge deformation in the camera captured images, variety in nature...
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Design of an Optical Character Recognition System for Camera-based Handheld Devices
This paper presents a complete Optical Character Recognition (OCR) syste...
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Text Region Extraction from Business Card Images for Mobile Devices
Designing a Business Card Reader (BCR) for mobile devices is a challenge...
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Text Extraction From Texture Images Using Masked Signal Decomposition
Text extraction is an important problem in image processing with applica...
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One Shot 3D Photography
3D photography is a new medium that allows viewers to more fully experie...
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Text/Graphics Separation and Skew Correction of Text Regions of Business Card Images for Mobile Devices
Separation of the text regions from background texture and graphics is an important step of any optical character recognition system for the images containing both texts and graphics. In this paper, we have presented a novel text/graphics separation technique and a method for skew correction of text regions extracted from business card images captured with a cell-phone camera. At first, the background is eliminated at a coarse level based on intensity variance. This makes the foreground components distinct from each other. Then the non-text components are removed using various characteristic features of text and graphics. Finally, the text regions are skew corrected for further processing. Experimenting with business card images of various resolutions, we have found an optimum performance of 98.25 takes 0.17 seconds processing time and 1.1 MB peak memory on a moderately powerful computer (DualCore 1.73 GHz Processor, 1 GB RAM, 1 MB L2 Cache). The developed technique is computationally efficient and consumes low memory so as to be applicable on mobile devices.
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