A Contour-Based Robust Algorithm for Text Detection in Color Images

Liu, Yangxing; Goto, Satoshi; Ikenaga, Takeshi

doi:10.1093/ietisy/e89-d.3.1221

Cited by 55 publications

(24 citation statements)

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“…Hybrid methods [27,54] are a combination of texture based methods and component based methods, which make use of the advantages of these two types of methods. In the method propsed by Liu et al [54], edge pixels of all possible text regions were extracted using an elaborate edge detection strategy, and the gradient and geometrical properties of region contours are verified to generate candidate text regions, followed by a texture analysis procedure to distinguish true text regions from non-text regions.…”

Section: Figmentioning

confidence: 99%

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Scene text detection and recognition: recent advances and future trends

Zhu

Yao

Bai

2015

Front. Comput. Sci.

356

158

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Text, as one of the most influential inventions of humanity, has played an important role in human life, so far from ancient times. The rich and precise information embodied in text is very useful in a wide range of vision-based applications, therefore text detection and recognition in natural scenes have become important and active research topics in computer vision and document analysis. Especially in recent years, the community has seen a surge of research efforts and substantial progresses in these fields, though a variety of challenges (e.g. noise, blur, distortion, occlusion and variation) still remain. The purposes of this survey are three-fold: 1) introduce up-to-date works, 2) identify state-of-the-art algorithms, and 3) predict potential research directions in the future. Moreover, this paper provides comprehensive links to publicly available resources, including benchmark datasets, source codes, and online demos. In summary, this literature review can serve as a good reference for researchers in the areas of scene text detection and recognition.

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Section: Figmentioning

confidence: 99%

“…[54], the hybrid method proposed by Pan et al [27] extracts candidate components from multi-scale probability maps. The probability maps are estimated by a classifier, which is trained on a set of texture features (HOG features [55]) computed with a group of predefined patterns.…”

Section: Figmentioning

confidence: 99%

Scene text detection and recognition: recent advances and future trends

Zhu

Yao

Bai

2015

Front. Comput. Sci.

356

158

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“…Lack of precision, computational complexity, problems with information integration are the limitations of this method. Region based methods are discussed in [6,10]. Pixels exhibiting certain properties such as approximately constant color are grouped together.…”

Section: Previous Workmentioning

confidence: 99%

A Wavelet based Method for Text Segmentation in Color Images

Priya¹,

Gobu²

2013

IJCA

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This is a digital era, in which imaging has become simple and easy with so many handheld compact devices. Extraction of the text from captured images also becomes necessary for efficient indexing and retrieval purpose. But real time images have issues such as sensor noise, blur, viewing angle, different font size, low contrast etc. Though so many efficient methods exist, they faultier when it comes to complex background images. A wavelet based method is used here. This method preprocesses the image before it is given to an OCR filter. A color quantizer is used to minimize the number of distinct colors in the input image. A discrete wavelet transform is performed on the color quantized image which classifies the image into text and nontext pixels based on their color and the standard deviation of the wavelet. This step is followed by fuzzy C means clustering which partitions the image into the background and text regions. This preprocessed image is then passed through an OCR filter to check the quality of text being segmented.

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“…The Literature has been surveyed to find existing methods for text extraction from different kinds of images which are [2] based on the combination of connected component and texture feature analysis of unknown text region contours. Each candidate text region is verified with texture features derived from wavelet domain followed by expectation maximization algorithm to binarise each text region.…”

Section: Related Workmentioning

confidence: 99%

Statistical modeling for the detection, localization and extraction of text from heterogeneous textual images using combined feature scheme

Gopalan

Manjula

2010

SIViP

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Discriminating between the text and non text regions of an image is a complex and challenging task. In contrast to Caption text, Scene text can have any orientation and may be distorted by the perspective projection. Moreover, it is often affected by variations in scene and camera parameters such as illumination, focus, etc. These variations make the design of unified text extraction from various kinds of images extremely difficult. This paper proposes a statistical unified approach for the extraction of text from hybrid textual images (both Scene text and Caption text in an image) and Document images with variations in text by using carefully selected features with the help of multi level feature priority (MLFP) algorithm. The selected features are combinedly found to be the good choice of feature vectors and have the efficacy to discriminate between text and non text regions for Scene text, Caption text and Document images and the proposed system is robust to illumination, transformation/perspective projection, font size and radially changing/angular text. MLFP feature selection algorithm is evaluated with three common ML algorithms: a decision tree inducer (C4.5), a naive Bayes classifier, and an instance based K-nearest neighbour learner and effectiveness of MLFP is shown by comparing with three

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A Contour-Based Robust Algorithm for Text Detection in Color Images

Cited by 55 publications

References 0 publications

Scene text detection and recognition: recent advances and future trends

Scene text detection and recognition: recent advances and future trends

A Wavelet based Method for Text Segmentation in Color Images

Statistical modeling for the detection, localization and extraction of text from heterogeneous textual images using combined feature scheme

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