• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.94-98
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• 2009

A video caption recognition system has been developed for broadcasting sport events such as major league baseball. The purpose of the system is to translate the information expressed in English units such as miles per hour (MPH) to the international system of units (SI) such as km/h. The system detects the ball speed displayed in the video and recognizes the numerals. The ball speed is then converted to km/h and displayed by the following character generator (CG) system. Although neural-network based methods are widely used for character and numeral recognition, we use template matching to avoid the training process required before the broadcasting. With the proposed template matching method, the operator can cope with the situation when the caption’s appearance changed without any notification. Templates are configured by the operator with a captured screenshot of the first pitch with ball speed. Templates are updated with following correct recognition results. The accuracy of the recognition module is over 97%, which is still not enough for live broadcasting. When the recognition confidence is low, the system asks the operator for the correct recognition result. The operator chooses the right one using hot keys.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.689-697
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• 2010

In this study, we propose a new procedure for improving the character recognition of text area extracted from video images. The recognition of strings extracted from video, which are mixed with Hangul, English, numbers and special characters, etc., is more difficult than general character recognition because of various fonts and size, graphic forms of letters tilted image, disconnection, miscellaneous videos, tangency, characters of low definition, etc. We improved the recognition rate by taking commonly used letters and leaving out the barely used ones instead of recognizing all of the letters, and then using SVM and RDA character recognition methods. Our numerical results indicate that combining SVM and RDA performs better than other methods.

• The Journal of Korean Association of Computer Education
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• v.5 no.4
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• pp.134-145
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• 2002

The purpose of this study is to present the functional and technical solutions for text learning of web-based textbook in which each letter has its own focal point. The solutions help learners not to lose the main focus when eye moves to the next letter or line. The text screen of the electronic textbook automatically scrolls the text to up and down or left and right directions which are preassigned by learner. It doesn't need the operation of mouse or keyboard. And learner can change scroll speed and types anytime during scrolling. Automatic text scroll function is a solution for controlling data and screen to reflect the personal favor and ability. It contains the content structure of the text(characteristics, categorizations etc.), the appearance of the text(density, size, font etc.), scroll options(scroll, speed etc.), program control type(ram resident program etc.), and the application of the screen design principles(legibility etc.). To resolve these functional problems, technical 8 phases are provided, which are environment setting, scroll option setting, copy, data analysis, scroll coding, centered focus coding, left and right focus coding, implementation. The learner can focus on text without dispersion because the text focal points stay in the fixed area of screen. 1bey read the text following their preferences for fonts, sizes, line spacing and so on.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.391-401
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• 2022

As deep learning-based object detection and recognition research have been developed recently, the scope of application to industry and real life is expanding. But deep learning-based systems in the construction system are still much less studied. Calculating materials in the construction system is still manual, so it is a reality that transactions of wrong volumn calculation are generated due to a lot of time required and difficulty in accurate accumulation. A fast and accurate automatic drawing recognition system is required to solve this problem. Therefore, we propose an AI-based automatic drawing recognition accumulation system that detects and recognizes steel materials in construction drawings. To accurately detect steel materials in construction drawings, we propose data augmentation techniques and spatial attention modules for improving small object detection performance based on YOLOv4. The detected steel material area is recognized by text, and the number of steel materials is integrated based on the predicted characters. Experimental results show that the proposed method increases the accuracy and precision by 1.8% and 16%, respectively, compared with the conventional YOLOv4. As for the proposed method, Precision performance was 0.938. The recall was 1. Average Precision AP_0.5 was 99.4% and AP_0.5:0.95 was 67%. Accuracy for character recognition obtained 99.9.% by configuring and learning a suitable dataset that contains fonts used in construction drawings compared to the 75.6% using the existing dataset. The average time required per image was 0.013 seconds in the detection, 0.65 seconds in character recognition, and 0.16 seconds in the accumulation, resulting in 0.84 seconds.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.499-508
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• 2022

A lot of research has been carried out on the Hangeul generation model using deep learning, and recently, research is being carried out how to minimize the number of characters input to generate one set of Hangul (Few-Shot Learning). In this paper, we propose a CKFont2 model using only 14 letters by analyzing and improving the CKFont (hereafter CKFont1) model using 28 letters. The CKFont2 model improves the performance of the CKFont1 model as a model that generates all Hangul using only 14 characters including 24 components (14 consonants and 10 vowels), where the CKFont1 model generates all Hangul by extracting 51 Hangul components from 28 characters. It uses the minimum number of characters for currently known models. From the basic consonants/vowels of Hangul, 27 components such as 5 double consonants, 11/11 compound consonants/vowels respectively are learned by deep learning and generated, and the generated 27 components are combined with 24 basic consonants/vowels. All Hangul characters are automatically generated from the combined 51 components. The superiority of the performance was verified by comparative analysis with results of the zi2zi, CKFont1, and MX-Font model. It is an efficient and effective model that has a simple structure and saves time and resources, and can be extended to Chinese, Thai, and Japanese.