• Journal of Digital Contents Society
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• v.7 no.1
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• pp.53-58
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• 2006

In this paper, We proposed extraction method using EZW a specific character and then add watermark significant coefficient of image. After wavelet transform in image, the significant coefficient value add to watermark information image. In this method, the locations of nonzero wavelet coefficients are encoded with a tree structure, called zerotree, which can exploit the self-similarity of the pyramid decomposition across different scales. The simulation shows that this method provides a superior performance over conventional method and can be successfully applied to the application areas that requires of progressive transmission and search for image data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.189-190
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• 2018

최근에는 여행에 대한 각종 정보가 많이 공유되는 추세이다. 최근 사람들은 소셜 네트워크 서비스를 이용 중이거나 웹 서핑을 하는 도중에 기억하고 싶어 하는 여행지를 단순히 캡처 해놓거나 메모장에 기록해둔다. 이러한 방법은 시간이 지나 많은 데이터가 쌓이면 관리하기 어렵다는 문제가 존재한다. 본 논문에서는 사용자의 편리를 고려하여 사진의 텍스트를 광학식 문자 판독을 활용하여 출력하고 게시 글 형태로 저장할 수 있게 개발하였다. 명소의 위치 또한 자동완성 위치 검색 라이브러리를 통하여 편리 저장이 가능하다. 위치 데이터를 통해 향후 사용자가 근접하고 있는 여행지 또한 제공해줄 수 있도록 구현하였다. 이를 위하여 웹을 통해서 이용할 수도 있으며 실시간 검색과 알림 이벤트를 위해 웹 주소 입력 없이도 앱을 실행할 수 있는 프로그래시브웹 앱을 구현하였다.

• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.10-18
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• 2010

Due to a large number of classes in Hangul character recognition, it is usual to use the six-type preclassification stage. After the preclassification, the first consonent, vowel, and last consonent can be classified separately. Though each of three components has a few of classes, classification errors occurs often due to shape similarity such as 'ㅔ' and 'ㅖ'. So this paper proposes a hierarchical recognition method which adopts multi-stage tree structures for each of 6-types. In addition, to reduce the interference among three components, the method uses the recognition results of first consonents and vowel as features of vowel classifier. The recognition accuracy for the test set of PHD08 database was 98.96%.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.1
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• pp.19-25
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• 2001

A novel approach for the feature extraction of handwritten characters is proposed by using spatial filtering neural networks with 4 layers. The proposed system first removes rough pixels which are easy to occur in handwritten characters. The system then extracts and removes the boundary information which have no influence on characters recognition. Finally, The system extracts feature information and removes the noises from feature information. The spatial filters adapted in the system correspond to the receptive fields of ganglion cells in retina and simple cells in visual cortex. With PE2 Hangul database, we perform experiments extracting features of handwritten characters recognition. It will be shown that the network can extract feature informations from handwritten characters successfully.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.596-599
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• 2001

Extracting of car licens plate is important for identifying the car. Since there are some problems such as poor ambient lighting problem, bad weather problem and so on, the car images we distorted and the tar license plate is difficult to be extracted. This paper proposes a method of extracting car license plate using motor vehicle regulation. In this method, some features of car license plate according to motor vehicle regulation such as color information, shape are applied to determine the candidate of car license plates. For the result of recognition by neural network, the candidate which has characters and numbers patterns according to motor vehicle regulation is certified as license-plate region. The results of the experiments with 70 samples of real car images shoe the performance of car license-plate extraction by 84.29%, and the recognition rate is 80.81%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.5
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• pp.887-894
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• 2006

This paper proposes a new method that detects the intersection points of handwritten Hangul strokes using run-length. The method firstly finds the strokes' width of handwritten Hangul characters using both horizontal and vertical run-lengths, secondly extracts horizontal and vertical strokes of a character utilizing the strokes' width, and finally detects the intersection points of the strokes exploiting horizontal and vertical strokes. The analysis of both the horizontal and the vertical strokes doesn't use the strokes' angles but both the strokes' width and the changes of the run-lengths. The intersection points of the strokes become the candidated parts for phoneme segmentation, which is one of main techniques for off-line handwritten Hangul recognition. The segmented strokes represent the feature for handwritten Hangul recognition.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.52-57
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• 2018

The purpose of this paper is to develop simulations that can be used for virtual education in dentistry. The virtual education to be developed will be developed with clinical training and actual case data of tooth extraction. This development goal is to allow dental students to learn the necessary surgical techniques at the point of their choice, not going into the operating room, away from time, space, and physical limits. I want to develop content using VR. Oculus Rift HMD, Optical Based Outside-in Tracking System, Oculus Touch Motion Controller, and Headset as Input / Output Device. In this configuration, the optimization method is applied convergent, and when the operation of the VR contents is performed, the content data is extracted from the interaction analysis formed in the VR engine, and the data is processed by the content algorithm. It also computes events and dental operations generated within the 3D engine programming and generates corresponding events through data processing according to the input signal. The visualization information is output to the HMD using the rendering information. In addition, the operating room environment was constructed by studying lighting and material for actual operating room environment. We applied the ratio of actual space to virtual space and the ratio between character and actual person to create a spatial composition at a similar rate to actual space.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.95-101
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• 2019

Recently, there have been many issues related to text recognition using Tesseract. One of these issues is that the text recognition accuracy is significantly lower for smaller fonts. Tesseract extracts text by creating an outline with direction in the image. By searching the Tesseract database, template matching with characters with similar feature points is used to select the character with the lowest error. Because of the poor text extraction, the recognition accuracy is lowerd. In this paper, we compared text recognition accuracy after applying various super-resolution methods to smaller text images and experimented with how the recognition accuracy varies for various image size. In order to recognize small Korean text images, we have used super-resolution algorithms based on deep learning models such as SRCNN, ESRCNN, DSRCNN, and DCSCN. The dataset for training and testing consisted of Korean-based scanned images. The images was resized from 0.5 times to 0.8 times with 12pt font size. The experiment was performed on x0.5 resized images, and the experimental result showed that DCSCN super-resolution is the most efficient method to reduce precision error rate by 7.8%, and reduce the recall error rate by 8.4%. The experimental results have demonstrated that the accuracy of text recognition for smaller Korean fonts can be improved by adding super-resolution methods to the OCR preprocessing module.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.220-228
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• 2022

Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding's plays a vital role in road safety measures. At initial stage applying preprocessing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies.

• The KIPS Transactions:PartB
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• v.10B no.4
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• pp.443-450
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• 2003

In this paper, we proposed the novel hierarchical algorithm for the recognition of English calling cards that processes multiresolution images of calling cards hierarchically to extract individual characters and recognizes the extracted characters by using the enhanced neural network method. The hierarchical recognition algorithm generates multiresolution images of calling cards, and each processing step in the algorithm selects and processes the image with suitable resolution for lower processing overhead and improved output. That is, first, the image of 1/3 times resolution, to which the horizontal smearing method is applied, is used to extract the areas including only characters from the calling card image, and next, by applying the vertical smearing and the contour tracking masking, the image of a half time resolution is used to extract individual characters from the character string areas. Lastly, the original image is used in the recognition step, because the image includes the morphological information of characters accurately. And for the recognition of characters with diverse font types and various sizes, the enhanced RBF network that improves the middle layer based on the ART1 was proposed and applied. The results of experiments on a large number of calling card images showed that the proposed algorithm is greatly improved in the performance of character extraction and recognition compared with the traditional recognition algorithms.