Text segmentation in color images using tensor voting

Lim, JaeGuyn; Park, Jong‐Hyun; Medioni, Gérard

doi:10.1016/j.imavis.2006.05.011

Cited by 36 publications

(30 citation statements)

References 19 publications

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“…False positives and false negatives rate could be decreased by applying more sophisticated scene text extraction techniques. 23 Our method for finding tilt angle of the text works well. However, in addition to rotation, real life images are often affected by perspective effects, because users are rarely perfectly facing the text they are photographing.…”

Section: Future Workmentioning

confidence: 90%

Image-based mobile service: automatic text extraction and translation

et al. 2010

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We present a new mobile service for the translation of text from images taken by consumer-grade cell-phone cameras. Such capability represents a new paradigm for users where a simple image provides the basis for a service. The ubiquity and ease of use of cell-phone cameras enables acquisition and transmission of images anywhere and at any time a user wishes, delivering rapid and accurate translation over the phone's MMS and SMS facilities. Target text is extracted completely automatically, requiring no bounding box delineation or related user intervention. The service uses localization, binarization, text deskewing, and optical character recognition (OCR) in its analysis. Once the text is translated, an SMS message is sent to the user with the result. Further novelties include that no software installation is required on the handset, any service provider or camera phone can be used, and the entire service is implemented on the server side.

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Section: Future Workmentioning

confidence: 90%

Image-based mobile service: automatic text extraction and translation

et al. 2010

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“…To analyze and detect corrupted regions appearing as small areas in the image, different image regions having different characteristics should be represented in separate surfaces in the 3D spaces of the tensor voting domain. The work in [23], a tensor voting-based image segmentation method, is the closest to ours. In [23], a chromaticity image serving as token data is created, based on the values of the pixels in the input image.…”

Section: Token Generationmentioning

confidence: 98%

“…The work in [23], a tensor voting-based image segmentation method, is the closest to ours. In [23], a chromaticity image serving as token data is created, based on the values of the pixels in the input image. Since the value in the chromaticity image is real, pixels that belong to different objects or layers are not well separated.…”

Section: Token Generationmentioning

confidence: 98%

Automatically improving image quality using tensor voting

Nguyên

Park

Kim

et al. 2010

Neural Comput & Applic

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A novel corrupted region detection technique based on tensor voting is proposed to automatically improve the image quality. This method is suitable for restoring degraded images and enhancing binary images. First, the input images are converted into layered images in which each layer contains objects having similar characteristics. By encoding the pixels in the layered images with second-order tensors and performing voting among them, the corrupted regions are automatically detected using the resulting tensors. These corrupted regions are then restored to improve the image quality. The experimental results obtained from automatic image restoration and binary image enhancement applications show that our method can successfully detect and correct the corrupted regions.

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“…Tai et al [33] use the color gradient and local statistics in order to increase the resolution of images. More recently, Lim et al [34] extract text from color images by applying the TVF on 3D tensors created from the pixel's position (row and column) and a single value calculated from its HSI color components. The results of those schemes based on the second strategy have shown that the TVF can be successful with color information.…”

Section: Previous Related Workmentioning

confidence: 99%

Edge-preserving color image denoising through tensor voting

Moreno

García

Puig

et al. 2011

Computer Vision and Image Understanding

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This paper presents a new method for edge-preserving color image denoising based on the tensor voting framework, a robust perceptual grouping technique used to extract salient information from noisy data. The tensor voting framework is adapted to encode color information through tensors in order to propagate them in a neighborhood by using a specific voting process. This voting process is specifically designed for edge-preserving color image denoising by taking into account perceptual color differences, region uniformity and edginess according to a set of intuitive perceptual criteria. Perceptual color differences are estimated by means of an optimized version of the CIEDE2000 formula, while uniformity and edginess are estimated by means of saliency maps obtained from the tensor voting process. Measurements of removed noise, edge preservation and undesirable introduced artifacts, additionally to visual inspection, show that the proposed method has a better performance than the state-of-the-art image denoising algorithms for images contaminated with CCD camera noise.

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Text segmentation in color images using tensor voting

Cited by 36 publications

References 19 publications

Image-based mobile service: automatic text extraction and translation

Image-based mobile service: automatic text extraction and translation

Automatically improving image quality using tensor voting

Edge-preserving color image denoising through tensor voting

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