• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.71-77
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• 2014

In this paper, we propose an improved numeric character recognition method which can recognize numeric characters well under low-illuminated and shade-illuminated environment. The LN(Local Normalization) preprocessing method is used in order to enhance low-illuminated and shade-illuminated image quality. The reading area is detected using line segment information extracted from the illumination-normalized meter images, and then the three-phase procedures are performed for segmentation of numeric characters in the reading area. Finally, an efficient hybrid classifier is used to classify the segmented numeric characters. The proposed numeric character classifier is a combination of multi-layered feedforward neural network and template matching module. Robust heuristic rules are applied to classify the numeric characters. Experiments using meter image database were conducted. Meter image database was made using various kinds of meters under low-illuminated and shade-illuminated environment. The experimental results indicates the superiority of the proposed numeric character recognition method.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.89-96
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• 2003

This paper propose the new method witch is adopted in extracting character features and recognizing numeric characters using fractal dimension and modified Henon Attractor of the Chaos Theory. Firstly, it gets features of mesh feature, projection feature and cross distance feature from numeric character images And their feature hi converted into time series data. Then using the modified Henon system suggested in this paper, it gets last features of numeric character image after calculating Natural Measure and information bit which art meant fractal dimension. Finally, numeric character recognition is performed by statistically finding out the each information bit showing the minimum difference against the normalized pattern database. An Experimental result shows 100% character classification rates for 10 digits and 90% of recognition rates in real situation and the recognition speed was 26 characters per second.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1871-1874
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• 2003

Knowledge-based numeric open caption recognition is proposed that can recognize numeric captions generated by character generator (CG) and automatically superimpose a modified caption using the recognized text only when a valid numeric caption appears in the aimed specific region of a live sportscast scene produced by other broadcasting stations. in the proposed method, mesh features are extracted from an enhanced binary image as feature vectors, then a valuable information is recovered from a numeric image by perceiving the character using a multiplayer perceptron (MLP) network. The result is verified using knowledge-based hie set designed for a more stable and reliable output and then the modified information is displayed on a screen by CG. MLB Eye Caption based on the proposed algorithm has already been used for regular Major League Base-ball (MLB) programs broadcast five over a Korean nationwide TV network and has produced a favorable response from Korean viewer.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.37-44
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• 2003

In this paper, we investigated the performance of both DNA coding method and Genetic Algorithm(GA) in numeric pattern (from 0 to 9) recognition. The performance of the DNA coding method is compared to the that of the GA. GA searches effectively an optimal solution via the artificial evolution of individual group of binary string using binary coding, while DNA coding method uses four-type bases denoted by Adenine(A), Cytosine(C), Guanine(G) and Thymine(T). To compare the performance of both method, the same genetic operators(crossover and mutation) are applied and the probabilities of crossover and mutation are set the same values. The results show that the DNA coding method has better performance over GA. The reasons for this outstanding performance are multiple candidate solution presentation in one string and variable solution string length.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.5-8
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• 2001

We propose a realtime numeric caption recognition algorithm that automatically recognizes the numeric caption generated by computer graphics (CG) and displays the modified caption using the recognized resource only when a valuable numeric caption appears in the aimed specific region of the live sportscast scene produced by other broadcasting stations. We extract the mesh feature from the enhanced binary image as a feature vector after acquiring the sports broadcast scenes using a frame grabber in realtime and then recover the valuable resource from just a numeric image by perceiving the character using the neural network. Finally, the result is verified by the knowledge-based rule set designed for more stable and reliable output and is displayed on a screen as the converted CC caption serving our purpose. At present, we have actually provided the realtime automatic mile-to-kilometer caption conversion system taking up our algorithm f3r the regular Major League Baseball (MLB) program being broadcasted live throughout Korea over our nationwide network. This caption conversion system is able to automatically convert the caption in mile universally used in the United States into that in kilometer in realtime, which is familiar to almost Koreans, and makes us get a favorable criticism from the TV audience.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.99-107
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• 2009

This paper presents an enhanced hybrid model based on Independent Component Analysis(ICA) in order to features of numeric characters in license plates. ICA which is used only in high dimensional statistical features doesn't consider statistical features in low dimension and correlation between numeric characters. To overcome the drawbacks of ICA, we propose an improved ICA with the hybrid model using both Principle Component Analysis(PCA) and Linear Discriminant Analysis(LDA). Experiment results show that the proposed model has a superior performance in feature extraction and recognition compared with ICA only as well as other hybrid models.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.3
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• pp.133-142
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• 2019

The existing auxiliary communicating aids for the hearing-impaired have an inconvenience of using additional expensive sensing devices. This paper presents a hand image detection based algorithm to interpret the sign language of the hearing-impaired. The proposed sign language recognition system exploits the hand image only captured by the camera without using any additional gloves with extra sensors. Based on the hand image processing, the system can perfectly classify several numeric sign language representations. This work proposes a simple lightweight classification algorithm to identify the hand image of the hearing-impaired to communicate with others even further in an environment of complex background. Experimental results show that the proposed system can interpret the numeric sign language quite well with an accuracy of 95.6% on average.

• Journal of the Korean Graphic Arts Communication Society
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• v.4 no.1
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• pp.19-26
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• 1986

• International conference on construction engineering and project management
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• 2020.12a
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• pp.313-322
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• 2020

The recognition of the risk hazards is a vital step to effectively prevent accidents on a construction site. The advanced development in computer vision systems and the availability of the large visual database related to construction site made it possible to take quick action in the event of human error and disaster situations that may occur during management supervision. Therefore, it is necessary to analyze the risk factors that need to be managed at the construction site and review appropriate and effective technical methods for each risk factor. This research focuses on analyzing Occupational Safety and Health Agency (OSHA) related to risk zone identification rules that can be adopted by the image recognition technology and classify their risk factors depending on the effective technical method. Therefore, this research developed a pattern-oriented classification of OSHA rules that can employ a large scale of safety hazard recognition. This research uses joint reasoning of risk zone Identification and numeric input by utilizing a stereo camera integrated with an image detection algorithm such as (YOLOv3) and Pyramid Stereo Matching Network (PSMNet). The research result identifies risk zones and raises alarm if a target object enters this zone. It also determines numerical information of a target, which recognizes the length, spacing, and angle of the target. Applying image detection joint logic algorithms might leverage the speed and accuracy of hazard detection due to merging more than one factor to prevent accidents in the job site.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.3
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• pp.1-7
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• 2007

In order to recognize a vehicle license plate, the region of the license plate should be extracted from a vehicle image. Then, character region should be separated from the background image and characters are recognized using some neural networks with selected feature vectors. Of course, choice of feature vectors which serve as the basis of the character recognition has an important effect on recognition result as well as reduction of data amount. In this paper, we propose a novel feature extraction method in which number images are decomposed into linear combination of eigennumbers and show the validity of this method by applying to the recognition of numeric characters in license plates. The experimental results show the recognition rate of 95.3% for about 500 vehicle images with multi-layer perceptron neural network in the eigennumber space. Compared with the conventional mesh feature, it shows a better recognition rate by 5%.