• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1641-1652
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• 2021

To analyze the crime scene, the role of digital evidence such as CCTV and black box is very important. Such digital evidence is often damaged due to device defects or intentional deletion. In this case, the deleted video can be restored by well-known techniques like the frame-based recovery method. Especially, the data such as the video can be generally fragmented and saved in the case of the memory used almost fully. If the fragmented video were recovered in units of images, the sequence of the recovered images may not be continuous. In this paper, we proposed a new video restoration method to match the sequence of recovered images. First, the images are recovered through a frame-based recovery technique. Then, after analyzing the time information marked on the images, the time information was extracted and recognized via optical character recognition (OCR). Finally, the recovered images are rearranged based on the time information obtained by OCR. For performance evaluation, we evaluate the recovery rate of our proposed video restoration method. As a result, it was shown that the recovery rate for the fragmented video was recovered from a minimum of about 47% to a maximum of 98%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.697-698
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• 2021

본 논문에서는 시각 장애우의 식품 안전성 증진을 위해 광학 문자 인식 (optical character recognition, OCR) 및 실시간 객체 인식 (you only look once, YOLO) 알고리즘에 기반한 식품의 유통기한 자동 알림 시스템을 제안한다. 제안하는 시스템은 1) 스마트폰 카메라를 통해 실시간으로 입력되는 영상에서 YOLO 알고리즘을 활용하여 유통기한으로 예측되는 이미지 영역을 검출하고, 2) 검출된 영역에서 OCR 알고리즘을 활용하여 유통기한 데이터를 추출하며, 3) 최종 추출된 유통기한 데이터를 음성으로 변환하여 시각 장애우에게 전달한다. 개발된 시스템은 유통기한 정보를 추출해서 사용자에게 전달하기까지 평균 약 7초 이내의 빠른 응답 속도를 보였으며, 62.8%의 객체 인식 정확도와 93.6%의 문자 인식 정확도를 보였다. 이러한 결과들은 제안하는 시스템을 시각 장애우들이 실용적으로 활용할 수 있다는 가능성을 보여준다.

• Journal of Korea Multimedia Society
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• v.18 no.8
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• pp.943-949
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• 2015

A reader usually spends a lot of time browsing and searching word meaning in a dictionary, internet or smart applications in order to find the unknown words. In this paper, we propose a method to compensate this drawback. The proposed method introduces a vocabulary upon recognizing a word or group of words that was captured by a smart phone camera. Through this proposed method, organizing and editing words that were captured by smart phone, searching the dictionary data using bisection method, listening pronunciation with the use of speech synthesizer, building and editing of vocabulary stored in database are given as the features. A smart phone application for organizing English words was established. The proposed method significantly reduces the organizing time for unknown English words and increases the English learning efficiency.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.439-444
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• 2008

본 논문은 한영 혼용 문서에서의 문자 분할을 위한 효율적인 언어 인식기를 고안하였다. 한영 혼용 문서를 스캔한 후, OCR(광학 문자 판독, Optical Character Recognition)을 할 때, 문자 분할의 중요성은 상당히 크다. 인식 없이 문자를 분할하는 external segmentation 방법에서는, 인식할 언어가 한글 혹은 영어인가에 따라 문자 분할 방법이 달라진다. 그러므로, 한영 혼용 이미지를 인식하기 위해서 문자 분할을 하기 전에 언어를 미리 결정해야 한다. 본 논문에서는 문자 분할 방법을 효율적으로 하기 위한 언어 인식기를 제안하고 그 방법을 적용하였다. 그 결과 한영 혼용된 책 이미지에서 94.09%의 문자 분할 성공률을 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.810-812
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• 2015

문자인식 즉 OCR(Optical Character Recognition)기술은 광학적으로 인식할 수 있는 문자를 컴퓨터가 읽을 수 있도록 하는 기술을 뜻한다. 문자인식의 근간이 되는 방법은 스트링 매칭 기법이 사용되어 왔지만 한글의 경우 자음, 모음, 자음 조합으로 만 가지 유형이 넘고, 더욱이 상용한자와 영어를 섞어 쓰기 때문에 오인식되는 경우가 많다. 본 논문에서는 한글이 수직선, 수평선, 사선과 같이 방향성이 강한 선소들로 구성되어 있다는 점을 이용하여 한글의 인식률을 높이는 방법을 제안하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.73-75
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• 2022

Scene Text Recognition is a technology used in the field of artificial intelligence that requires manless robot, automatic vehicles and human-computer interaction. Though scene text images are distorted by noise interference, such as illumination, low resolution and blurring. Unlike previous studies that recognized only English, this paper shows a strong recognition accuracy including various characters, English, Korean, special character and numbers. Instead of selecting only one class having the highest probability value, a candidate word can be generated by considering the probability value of the second rank as well, thus a method can be corrected an existing language misrecognition problem.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.284-287
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• 2003

In recent years, most of the researches on pattern recognition are for medical diagnosis or for characters recognition. In fact its applications are very wide. In this paper, the pattern recognition is employed by linguistic translation, i.e. the output of Pattern Recognition is translated into another language. In this paper, it focuses on several fields: (1) System overview-explicate the functions of each part individually; (2) Criteria on the system-discuss the difficulties in each part; (3) System implementation-discuss how to design the approaches for constructing the system. Furthermore, intelligent approaches are considered be use on the system in different parts. They are discussed in the late paper, and also we concentrate on user interface, which can make a serious of processes in order, and easy control-just only pressing a few buttons. It is a new and creative attempt in digital system.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.5
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• pp.53-63
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• 2019

To provide a hand gesture interface through deep learning in mobile environments, research on the light-weighting of networks is essential for high recognition rates while at the same time preventing degradation of execution speed. This paper proposes a method of real-time recognition of written characters in the air using a finger on mobile devices through the light-weighting of deep-learning model. Based on the SSD (Single Shot Detector), which is an object detection model that utilizes MobileNet as a feature extractor, it detects index finger and generates a result text image by following fingertip path. Then, the image is sent to the server to recognize the characters based on the learned OCR model. To verify our method, 12 users tested 1,000 words using a GALAXY S10+ and recognized their finger with an average accuracy of 88.6%, indicating that recognized text was printed within 124 ms and could be used in real-time. Results of this research can be used to send simple text messages, memos, and air signatures using a finger in mobile environments.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.101-108
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• 2024

AI-OCR (Artificial Intelligence Optical Character Recognition) combines OCR technology with Artificial Intelligence to overcome limitations that required human intervention. To enhance the performance of AI-OCR, training on diverse data sets is essential. However, the recognition rate declines when image colors have similar brightness levels. To solve this issue, this study employs Homomorphic filtering as a preprocessing step to clearly differentiate color levels, thereby increasing text recognition rates. While Homomorphic filtering is ideal for text extraction because of its ability to adjust the high and low frequency components of an image separately using a gamma value, it has the downside of requiring manual adjustments to the gamma value. This research proposes a range for gamma threshold values based on tests involving image contrast, brightness, and entropy. Experimental results using the proposed range of gamma values in Homomorphic filtering suggest a high likelihood for effective AI-OCR performance.

• ETRI Journal
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• v.32 no.5
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• pp.729-739
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• 2010

Due to the rapid development of mobile devices equipped with cameras, instant translation of any text seen in any context is possible. Mobile devices can serve as a translation tool by recognizing the texts presented in the captured scenes. Images captured by cameras will embed more external or unwanted effects which need not to be considered in traditional optical character recognition (OCR). In this paper, we segment a text image captured by mobile devices into individual single characters to facilitate OCR kernel processing. Before proceeding with character segmentation, text detection and text line construction need to be performed in advance. A novel character segmentation method which integrates touched character filters is employed on text images captured by cameras. In addition, periphery features are extracted from the segmented images of touched characters and fed as inputs to support vector machines to calculate the confident values. In our experiment, the accuracy rate of the proposed character segmentation system is 94.90%, which demonstrates the effectiveness of the proposed method.