A method for character string extraction using local and global segment crowdedness

Shiku, Osamu; Kawasue, Kikuhito; Nakamura, Akira

doi:10.1109/icpr.1998.711879

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…The extraction of the character regions from the extracted character element requires the application of the concept of areal density of segments, suggested in [6]. In [6], the character elements were converted into segments in order to calculate the areal density.…”

Section: Character Region Extractionmentioning

confidence: 99%

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Word Image Decomposition from Mixed Text/Graphics Images Using Statistical Methods

Jeong

Kim

2007

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)

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This paper describes the development and implementation of a algorithm to extract words from image regions mixed text/graphics in document images using statistical analyses, which is a component of DIPS(Document Images Processing System) using statistical methods. To extract word images from image regions, the character components need to be separated from graphic components. For this process, we propose a method to separate them with an analysis of box-plot using a statistics of structural components. An accuracy of this method is not sensitive to the changes of images because the criterion of separation is defined by the statistics of components. And then the character regions are determined by analyzing a local crowdedness of the separated character elements. Finally, we divide the character regions into text lines and word images using projection profile analysis and gap clustering, etc. The proposed system could reduce the influence resulted from the changes of images because it uses the criterion based on the statistics of image regions.

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Section: Character Region Extractionmentioning

confidence: 99%

“…In [6], the character elements were converted into segments in order to calculate the areal density. In this study, however, the BB of the character element is used without conversion.…”

Section: Character Region Extractionmentioning

confidence: 99%

Word Image Decomposition from Mixed Text/Graphics Images Using Statistical Methods

Jeong

Kim

2007

Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007)

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“…Previously described methods, that deal with direction ambiguity and disjoined letter-location, are based on Hough transformation [12], which requires intensive computing time and high memory usage, which are not suitable for mobile devices.…”

Section: Related Workmentioning

confidence: 99%

A Fast Algorithm for Korean Text Extraction and Segmentation from Subway Signboard Images Utilizing Smartphone Sensors

Milevskiy¹,

Ha²

2011

Journal of Computing Science and Engineering

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We present a fast algorithm for Korean text extraction and segmentation from subway signboards using smart phone sensors in order to minimize computational time and memory usage. The algorithm can be used as preprocessing steps for optical character recognition (OCR): binarization, text location, and segmentation. An image of a signboard captured by smart phone camera while holding smart phone by an arbitrary angle is rotated by the detected angle, as if the image was taken by holding a smart phone horizontally. Binarization is only performed once on the subset of connected components instead of the whole image area, resulting in a large reduction in computational time. Text location is guided by user's marker-line placed over the region of interest in binarized image via smart phone touch screen. Then, text segmentation utilizes the data of connected components received in the binarization step, and cuts the string into individual images for designated characters. The resulting data could be used as OCR input, hence solving the most difficult part of OCR on text area included in natural scene images. The experimental results showed that the binarization algorithm of our method is 3.5 and 3.7 times faster than Niblack and Sauvola adaptive-thresholding algorithms, respectively. In addition, our method achieved better quality than other methods.

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“…Step 3: Calculate the local segment densities [5] of lei, and then roughly separate lei into character areas. This step corresponds to the process (4) in Fig.…”

mentioning

confidence: 99%

A pattern recognition method of mathematical graphs

Takagi

2010

2010 IEEE International Conference on Systems, Man and Cybernetics

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A computer-aided system for transformation of mathematical figures into tactile graphics is useful for visually impaired students when they learn mathematics and science.To develop such a system, research on mathematical figure recoguition techniques is needed. It is natural to assume that (1) mathematical figures include characters and formulas to explain lines or curves that are graphical expressions of functions or equations, and (2) graphs are sometimes drawn using broken lines. Under these assumptions, this paper discusses a method of separating a mathematical figure into character components and graph components, and a classification method of broken lines.

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A method for character string extraction using local and global segment crowdedness

Cited by 6 publications

References 4 publications

Word Image Decomposition from Mixed Text/Graphics Images Using Statistical Methods

Word Image Decomposition from Mixed Text/Graphics Images Using Statistical Methods

A Fast Algorithm for Korean Text Extraction and Segmentation from Subway Signboard Images Utilizing Smartphone Sensors

A pattern recognition method of mathematical graphs

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