Document segmentation using texture variance and low resolution images

Murguía, Mario Ignacio Chacón

doi:10.1109/iai.1998.666879

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Quantitative and qualitative validations of our method are relatively comparable to the methods proposed by Keechul et al [2] , Xi et al [20] , and Liu et al [6] . The standard measures such as precision and recall discussed in [11,21] have been considered for comparing the performance of the proposed method and are computed by using the following formulas. From the experimental results shown in Table 1, it can be observed that the use of cellular automata in designing a method for text extraction is comparable to the methods being compared here.…”

Section: Resultsmentioning

confidence: 99%

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Text extraction and enhancement of binary images using cellular automata

Sahoo¹,

Kumar

Raina

et al. 2009

Int. J. Autom. Comput.

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Text characters embedded in images represent a rich source of information for content-based indexing and retrieval applications. However, these text characters are difficult to be detected and recognized due to their various sizes, grayscale values, and complex backgrounds. Existing methods cannot handle well those texts with different contrast or embedded in a complex image background. In this paper, a set of sequential algorithms for text extraction and enhancement of image using cellular automata are proposed. The image enhancement includes gray level, contrast manipulation, edge detection, and filtering. First, it applies edge detection and uses a threshold to filter out for low-contrast text and simplify complex background of high-contrast text from binary image. The proposed algorithm is simple and easy to use and requires only a sample texture binary image as an input. It generates textures with perceived quality, better than those proposed by earlier published techniques. The performance of our method is demonstrated by presenting experimental results for a set of text based binary images. The quality of thresholding is assessed using the precision and recall analysis of the resultant text in the binary image.

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Section: Resultsmentioning

confidence: 99%

“…The resulting image is a grey-scale image, i.e., it has only luminosity/brightness attribute, and hence, it is easier to work on it, as suggested by M. Murguia [11] .…”

Section: Luminance Computing For Each Pixelmentioning

confidence: 98%

Text extraction and enhancement of binary images using cellular automata

Sahoo¹,

Kumar

Raina

et al. 2009

Int. J. Autom. Comput.

View full text Add to dashboard Cite

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“…According to the first approach, pixels are segmented into text or non-text on the basis of texture information computed in the neighborhood of each pixel [6], [7]. Finding the thresholds for the classification is a big problem in this.…”

Section: Challenges and Related Workmentioning

confidence: 99%

Text Extraction from Document Images Using Edge Information

Grover

Arora

Mitra

2009

2009 Annual IEEE India Conference

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Detection of text from documents in which text is embedded in complex colored document images is a very challenging problem. There are a lot of potential uses of text extraction in image searching, archiving documents etc. In this paper, we propose a simple edge based feature to perform this task. It aims at detecting textual regions from the document and separating it from the graphics portion. The algorithm is based on the sharp edges of the characters which are missing in images. We find these edges and use them to classify text from images. This edge information can also be used for other image interpretation tasks.

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