A hybrid text segmentation approach

Li, Xiaojun; Wang, Weiqiang; Huang, Qingming; Gao, Wen; Qing, Laiyun

doi:10.1109/icme.2009.5202545

Cited by 12 publications

(3 citation statements)

References 9 publications

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“…Skeletonization From our previous work [8] and most of the papers [13] addresses the high density location to join close neighbors to create candidate text block. This feature is used in the next steps of our proposed algorithm.…”

Section: B Connected Component Analysis Based Onmentioning

confidence: 99%

Multi-Orientation Text Detection by Skeletonization (MOTDS)

Azadboni

Samadhiya

Khatri

2014

2014 2nd International Symposium on Computational and Business Intelligence

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In this paper, we propose a method that is able to handle diverse scene images with different font sizes, orientation and background complexity. For this, we use canny edge detection to localize high density location in image and dilate high density pixels by extending pixels environments to create connected components. Then try to blocking each connected component by skeleton structure. The next step result a proper foreground which is separated from the background. Then, in order to output only the text part, some significant features such as projection, corner density and variance are applied to each candidate blocks to recognize them as text or non-text blocks. The experimental results show that our proposed approach is robust enough to face the complex background.

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Section: B Connected Component Analysis Based Onmentioning

confidence: 99%

Multi-Orientation Text Detection by Skeletonization (MOTDS)

Azadboni

Samadhiya

Khatri

2014

2014 2nd International Symposium on Computational and Business Intelligence

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“…All known research did not discuss the white text printed on reflected material, so comparison is applied to work presented in this research with relevant systems presented in other literatures [11][12][13][44][45][46][47][48][49][50][51]. A comparison to their systems (as they reported), mostly from the last five years, as shown in Table 11 demonstrates that the presented system produced better results especially when compared with other related,same language, recognition systems as in [12,[44][45][46][47][48]51].…”

Section: Computer Simulations and Comparisonmentioning

confidence: 99%

Practical Recognition System for Text Printed on Clear Reflected Material

Mohammad

Agaian

2012

ISRN Machine Vision

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Text embedded in an image contains useful information for applications in the medical, industrial, commercial, and research fields. While many systems have been designed to correctly identify text in images, no work addressing the recognition of degraded text on clear plastic has been found. This paper posits novel methods and an apparatus for extracting text from an image with the practical assumption: (a) poor background contrast, (b) white, curved, and/or differing fonts or character width between sets of images, (c) dotted text printed on curved reflective material, and/or (d) touching characters. Methods were evaluated using a total of 100 unique test images containing a variety of texts captured from water bottles. These tests averaged a processing time of ~10 seconds (using MATLAB R2008A on an HP 8510 W with 4 G of RAM and 2.3 GHz of processor speed), and experimental results yielded an average recognition rate of 90 to 93% using customized systems generated by the proposed development.

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“…Li et al [4] proposed a hybrid text segmentation approach to extract text in images. First a new stroke edge filter is applied to retrieve stroke edge maps.…”

Section: Introductionmentioning

confidence: 99%

A new video text extraction method based on stroke

Huang

Wang

Zhu

et al. 2013

2013 6th International Congress on Image and Signal Processing (CISP)

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The video text is an important clue to infer semantic information of video. The accuracy of text extraction is crucial to the video text retrieval. Most of previous proposed methods can not completely extract the text characters when the text blocks own complicated background or when the stroke width of the text character is unpredictable. In this paper, we put forward a text extraction method, which can accurately extract video texts with complex background and various text character sizes. Experiments are conducted on TV news videos, which show that the performance of the proposed method is encouraging.

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A hybrid text segmentation approach

Cited by 12 publications

References 9 publications

Multi-Orientation Text Detection by Skeletonization (MOTDS)

Multi-Orientation Text Detection by Skeletonization (MOTDS)

Practical Recognition System for Text Printed on Clear Reflected Material

A new video text extraction method based on stroke

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