Segmentation of Road Guidance Sign Symbols and Characters Based on Normalized RGB Chromaticity Diagram

Soetedjo, Aryuanto; Yamada, Kôichi; Limpraptono, F. Yudi

doi:10.5120/716-1008

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Also, the car checks the prescheduled routes with the automatic recognition of route information in autonomous driving. Soetedjo et al (2010) developed an algorithm for extracting the symbols and text regions from road signs with a normalized RGB chromaticity diagram by detecting only the route number area, without recognizing the number itself. Vavilin and Jo (2009) extracted some route numbers, arrow regions, and texts with colour segmentation and geometry.…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Route Information from Road Sign Imagery

Youn¹,

Chong²

2015

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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With the advances of the big-data process technology, acquiring the real-time information from the massive image data taken by a mobile device inside a vehicle will be possible in the near future. Among the information that can be found around the vehicle, the route information is needed for safe driving. In this study, the automatic extraction of route information from the road sign imagery was dealt with. The scope of the route information in this study included the route number, route type, and their relationship with the driving direction. For the recognition of the route number, the modified Tesseract OCR (Optical Character Recognition) engine was used after extracting the rectangular-road-sign area with the Freeman chain code tracing algorithm. The route types (expressway, highway, rural highway, and municipal road) are recognized using the proposed algorithms, which are acquired from colour space analysis. Those road signs provide information about the route number as well as the roads that may be encountered along the way. In this study, such information was called "OTW (on the way)" or "TTW (to the way)" which between the two should be indicated is determined using direction information. Finally, the route number is matched with the direction information. Experiments are carried out with the road sign imagery taken inside a car. As a result, route numbers, route number type, OTW or TTW are successfully recognized, however some errors occurred in the process of matching TTW number with the direction. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Route Information from Road Sign Imagery

Youn¹,

Chong²

2015

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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Detection of traffic signs based on visual saliency model

Guo

Liu

2011

2011 International Conference on Multimedia Technology

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Gradation Image Processing for Text Recognition in Road Signs Using Image Division and Merging

Chong¹

2014

J. Korea Inst. Intelligent. Transp. Syst.

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This paper proposes a gradation image processing method for the development of a Road Sign Recognition Platform (RReP), which aims to facilitate the rapid and accurate management and surveying of approximately 160,000 road signs installed along the highways, national roadways, and local roads in the cities, districts (gun), and provinces (do) of Korea. RReP is based on GPS(Global Positioning System), IMU(Inertial Measurement Unit), INS(Inertial Navigation System), DMI(Distance Measurement Instrument), and lasers, and uses an imagery information collection/classification module to allow the automatic recognition of signs, the collection of shapes, pole locations, and sign-type data, and the creation of road sign registers, by extracting basic data related to the shape and sign content, and automated database design. Image division and merging, which were applied in this study, produce superior results compared with local binarization method in terms of speed. At the results, larger texts area were found in images, the accuracy of text recognition was improved when images had been gradated. Multi-threshold values of natural scene images are used to improve the extraction rate of texts and figures based on pattern recognition.

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Segmentation of Road Guidance Sign Symbols and Characters Based on Normalized RGB Chromaticity Diagram

Cited by 3 publications

References 12 publications

Automatic Extraction of Route Information from Road Sign Imagery

Automatic Extraction of Route Information from Road Sign Imagery

Detection of traffic signs based on visual saliency model

Gradation Image Processing for Text Recognition in Road Signs Using Image Division and Merging

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