Recognition of raised characters for automatic classification of rubber tires

Ham, Young Kug; Kang, Min Seok; Chung, Hong; Park, Rae-Hong; Park, Gwi Tae

doi:10.1117/12.184094

Cited by 31 publications

(22 citation statements)

References 0 publications

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“…The size, position, frequency, text alignment, and OCR-ed results can all be used for this. • Industrial automation: Part identiÿcation can be accomplished by using the text information on each part [80].…”

Section: Applicationsmentioning

confidence: 99%

Text information extraction in images and video: a survey

Jung¹,

Kim²,

Jain³

2004

Pattern Recognition

719

343

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Section: Applicationsmentioning

confidence: 99%

Text information extraction in images and video: a survey

Jung¹,

Kim²,

Jain³

2004

Pattern Recognition

719

343

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“…This dataset contains in total 89 images of logos, indoor scenes and street views and can be used for eval- 3) http://phys.org/news/2011-05-university-pennsylvania-pr2-robot-video.html 4) http://algoval.essex.ac.uk/icdar/Datasets.html 5) http://robustreading.opendfki.de/wiki/SceneText 6) http://dag.cvc.uab.es/icdar2013competition/ 7) http://media-lab.engr.ccny.cuny.edu/ cyi/project_scenetextdetection.html uating detection algorithms for multi-oriented texts in natural scenes.…”

Section: Benchmark Datasets • Icdar 2003 and 2005mentioning

confidence: 99%

“…This property makes text present in natural images and videos a special, important source of information. The rich and precise information embodied in text can be very beneficial to a variety of vision-based applications, such as image search [1], target geolocation [2], human-computer interaction [3], robot navigation [4] and industrial automation [5]. Consequently, automatic text detection and recognition, offering a means to access and utilize textual information in images and videos, have become active research topics in computer vision and document analysis.…”

Section: Introductionmentioning

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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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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“…To date, only a few literatures [1][2][3][4] appear on studying raised or indented characters. Moreover, most of the current study focuses on the feature extraction and recognition stage.…”

Section: Introductionmentioning

confidence: 99%

Contrast enhancement for images of raised characters on region of interest

Zhang

et al. 2010

2010 8th World Congress on Intelligent Control and Automation

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Raised or indented characters are widely used for recording the product information in the industrial field. However, images of such characters are of poor contrast because the characters have the same color and material as the background. It is this problem that makes recognition of such characters is difficult than that of conventional optical characters. To address this issue, the aim of this work is to investigate a contrast enhancement method applicable for raised or indented characters by carrying out an independent study on the illumination factor. Firstly, the illumination model of raised character is analyzed. Then, four different algorithms are introduced and the experiment was carried out with images of the raised characters pressed on metal label. Experiment results prove that homomorphic filtering is the most suitable contrast enhancement algorithm for raised or indented characters.

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Recognition of raised characters for automatic classification of rubber tires

Cited by 31 publications

References 0 publications

Text information extraction in images and video: a survey

Text information extraction in images and video: a survey

Scene text detection and recognition: recent advances and future trends

Contrast enhancement for images of raised characters on region of interest

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