• International journal of advanced smart convergence
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• v.8 no.3
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• pp.131-137
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• 2019

This study proposes a method of learning and recognizing the characteristics that are the classification criteria of Hangul using Mask R-CNN, one of the deep learning techniques, to recognize and classify atypical Hangul characters. The atypical characters on the Hangul signboard have a lot of deformed and colorful shapes beyond the general characters. Therefore, in order to recognize the Hangul signboard character, it is necessary to learn a separate atypical Hangul character rather than the existing formulaic one. We selected the Hangul character '닭' as sample data and constructed 5,383 Hangul image data sets and used them for learning and verifying the deep learning model. The accuracy of the results of analyzing the performance of the learning model using the test set constructed to verify the reliability of the learning model was about 92.65% (the area detection rate). Therefore we confirmed that the proposed method is very useful for Hangul signboard character recognition, and we plan to extend it to various Hangul data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2312-2325
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• 2013

In this paper, we propose a preprocessing method that it is to correct the distortion of text area in Korean signboard images as a preprocessing step to improve character recognition. Distorted perspective in recognizing of Korean signboard text may cause of the low recognition rate. The proposed method consists of four main steps and eight sub-steps: main step consists of potential vertical components detection, vertical components detection, text-boundary estimation and distortion correction. First, potential vertical line components detection consists of four steps, including edge detection for each connected component, pixel distance normalization in the edge, dominant-point detection in the edge and removal of horizontal components. Second, vertical line components detection is composed of removal of diagonal components and extraction of vertical line components. Third, the outline estimation step is composed of the left and right boundary line detection. Finally, distortion of the text image is corrected by bilinear transformation based on the estimated outline. We compared the changes in recognition rates of OCR before and after applying the proposed algorithm. The recognition rate of the distortion corrected signboard images is 29.63% and 21.9% higher at the character and the text unit than those of the original images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.2
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• pp.722-733
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• 2012

Recently various studies on various applications using images taken by mobile phone cameras have been actively conducted. This study proposes a correction method for misrecognition of Korean Texts in signboard images using improved Levenshtein metric. The proposed method calculates distances of five recognized candidates and detects the best match texts from signboard text database. For verifying the efficiency of the proposed method, a database dictionary is built using 1.3 million words of nationwide signboard through removing duplicated words. We compared the proposed method to Levenshtein Metric which is one of representative text string comparison algorithms. As a result, the proposed method based on improved Levenshtein metric represents an improvement in recognition rates 31.5% on average compared to that of conventional methods.

• The KIPS Transactions:PartB
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• v.16B no.3
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• pp.239-246
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• 2009

In this paper, we propose a Korean character recognition method from outboard signboard images. We have chosen 808 classes of Korean characters by an analysis of frequencies of appearance in a dictionary of signboard names. The proposed method mainly consists of three steps: feature extraction, rough classification, and coarse classification. The first step is to extract a nonlinear directional segments feature, which is immune to the distortion of character shapes. The second step computes an ordered set of 10 recognition candidates using a minimum distance classifier. The last step reorders the recognition candidates using a Fisher discriminant measure. As experimental results, the recognition accuracy is 80.45% for the first choice, and 93.51% for the top five choices.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.219-231
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• 2019

The specifications of signboards are set for each type of signboards, but the shape and size of the signboard actually installed are not uniform. In addition, because the colors of the signboard are not defined, so various colors are applied to the signboard. Methods for recognizing signboards can be thought of as similar methods of recognizing road signs and license plates, but due to the nature of the signboards, there are limitations in that the signboards can not be recognized in a way similar to road signs and license plates. In this study, we proposed a methodology for recognizing plate-type signboards, which are the main targets of illegal and old signboards, and automatically extracting areas of signboards, using the deep learning-based Faster R-CNN algorithm. The process of recognizing flat type signboards through signboard images captured by using smartphone cameras is divided into two sequences. First, the type of signboard was recognized using deep learning to recognize flat type signboards in various types of signboard images, and the result showed an accuracy of about 71%. Next, when the boundary recognition algorithm for the signboards was applied to recognize the boundary area of the flat type signboard, the boundary of flat type signboard was recognized with an accuracy of 85%.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.105-115
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• 2012

In this paper, we propose a correction method using phoneme unit segmentation to solve misrecognition of Korean Texts in signboard images using weighted Disassemble Levenshtein Distance. The proposed method calculates distances of recognized texts which are segmented into phoneme units and detects the best matched texts from signboard text database. For verifying the efficiency of the proposed method, a database dictionary is built using 1.3 million words of nationwide signboard through removing duplicated words. We compared the proposed method to Levenshtein Distance and Disassemble Levenshtein Distance which are common representative text string comparison algorithms. As a result, the proposed method based on weighted Disassemble Levenshtein Distance represents an improvement in recognition rates 29.85% and 6% on average compared to that of conventional methods, respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.197-209
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• 2020

Signboards are installed in different types and sizes depending on the shop characteristics. However, the local government is having difficulty managing signboards with frequent opening and closing of stores and insufficient management personnel. In this study, a methodology was proposed to standardize and efficiently manage signboard information. To this end, the signboard display method of the enforcement ordinance related to outdoor advertising was analyzed to define the attribute elements of standard signboard data. In addition, physical information of signboards was obtained through signboard recognition technology, which is a prior study, and attribute elements of signboard standard data were defined through information that can be read with the naked eye, building integration information of the Ministry of the Interior and Safety, and street name address. In order to standardize the signboard information by spatial characteristics, data product specifications and metadata were defined according to the national spatial information standard. Lastly, standard data for signboards were produced in XML (Extensible Markup Language) format for compatibility, and XSD (XML Schema Definition) was defined for XML integrity so that data validity could be verified. Through this, a standard data model for the informatization of signboards was designed.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.6
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• pp.1-8
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• 2018

In this paper, we implement the signboard voice guidance service for visually impaired person using virtual beacon. The new location to provide a location-based service can be added easily by using virtual beacons, which are locating by Wi-fi, GPS, and so on, instead of physical beacons, which are locating by physical devices. We provide the voice service for guiding information of the captured signboard for the visually impaired when he arrived at the location registered through the virtual beacon.

• The Journal of the Korea Contents Association
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• v.8 no.11
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• pp.10-17
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• 2008

The sign recognition and translation offer information and support decision making for foreigners or city tourist. Collecting sign images and building words in signs are essential to train machine recognizers and to evaluate systems. In this paper, we analyze the characteristics of sign images. The collected sign images are about 1000 captured from difference conditions and locations. We also build a dictionary of words in 100,000 sign names.

• Archives of design research
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• v.18 no.4 s.62
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• pp.287-296
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• 2005

The street image in a business area is so much affected by Facade that the front side of a roadside building makes. Recently, for the indiscreet and intemperate advertising signboard of the front side of roadside buildings, a streetscape becomes more disordered than before, so now we need to do research about signboards of roadside buildings for a streetscape image. In this research, we focused on a streetscape with difference of occupation density of signboard in the business area via investigation and analysis about occupation density of signboards of the front side of roadside buildings, and we suggested optimum occupation density of signboards for supporting the road image positively. An object of research is the street in the business area that has many pedestrians and active passing zone of cars. We investigated and analyzed how to feel street images on the rate of occupation density of roadside building's signboards of in the chosen street. As a result of using an adjective that we use for estimating street view images for extraction of street images, we could know 2 factors. We named that one is the image of recognition, and the other is the image of feelings. We knew that signboard density of street of heavily recognized images is from 20% to 30% and, signboard density of street of heavily feeling images is from 50% to 60%. We also could know that people feel both images of recognition and images of feeling in specific density, 30 to 50%. Through this result of research, we can suggest Facade on signboard density with the recognition and the feeling and use images of the street view as materials to be more specific and more special.