• Title/Summary/Keyword: character segmentation

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Study on Implementation of a neural Coprocessor for Printed Hangul-Character Recognition (한글 인쇄체 문자인식 전용 신경망 Coprocessor의 구현에 관한 연구)

  • Kim, Young-Chul;Lee, Tae-Won
    • The Transactions of the Korea Information Processing Society
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    • v.5 no.1
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    • pp.119-127
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    • 1998
  • In this paper, the design of a VLSI-based multilayer neural network is presented, which can be used as a dedicated hardware for character-type segmentation and character-element recogniti on consuming large processing time in conventional software-based Hangul printed-character recognition systems. Also the architecture and its design of a neural coprocessor interfacing the neural network with a host computcr and controlling thc neural network are presented. The architecture, behavior, and performance of the proposed neural coprocessor are justified using VHDL modeling and simulation. Experimental results show the successful rates of character-type segmentation and character-element recognition is competitive to those of software-based Hangul printed-character recognition systems with retaining high-speed.

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A Study on the License Plate Recognition Based on Direction Normalization and CNN Deep Learning (방향 정규화 및 CNN 딥러닝 기반 차량 번호판 인식에 관한 연구)

  • Ki, Jaewon;Cho, Seongwon
    • Journal of Korea Multimedia Society
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    • v.25 no.4
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    • pp.568-574
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    • 2022
  • In this paper, direction normalization and CNN deep learning are used to develop a more reliable license plate recognition system. The existing license plate recognition system consists of three main modules: license plate detection module, character segmentation module, and character recognition module. The proposed system minimizes recognition error by adding a direction normalization module when a detected license plate is inclined. Experimental results show the superiority of the proposed method in comparison to the previous system.

A Fast Algorithm for Korean Text Extraction and Segmentation from Subway Signboard Images Utilizing Smartphone Sensors

  • Milevskiy, Igor;Ha, Jin-Young
    • Journal of Computing Science and Engineering
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    • v.5 no.3
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    • pp.161-166
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    • 2011
  • We present a fast algorithm for Korean text extraction and segmentation from subway signboards using smart phone sensors in order to minimize computational time and memory usage. The algorithm can be used as preprocessing steps for optical character recognition (OCR): binarization, text location, and segmentation. An image of a signboard captured by smart phone camera while holding smart phone by an arbitrary angle is rotated by the detected angle, as if the image was taken by holding a smart phone horizontally. Binarization is only performed once on the subset of connected components instead of the whole image area, resulting in a large reduction in computational time. Text location is guided by user's marker-line placed over the region of interest in binarized image via smart phone touch screen. Then, text segmentation utilizes the data of connected components received in the binarization step, and cuts the string into individual images for designated characters. The resulting data could be used as OCR input, hence solving the most difficult part of OCR on text area included in natural scene images. The experimental results showed that the binarization algorithm of our method is 3.5 and 3.7 times faster than Niblack and Sauvola adaptive-thresholding algorithms, respectively. In addition, our method achieved better quality than other methods.

A Study on the Phoneme Segmentation of Handwritten Korean Characters by Local Graph Patterns on Contacting Points (접촉점에서의 국소 그래프 패턴에 의한 필기체 한글의 자소분리에 관한 연구)

  • 최필웅;이기영;구하성;고형화
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.30B no.4
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    • pp.1-10
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    • 1993
  • In this paper, a new method of phoneme segmentation of handwritten Korean characters using the local graph pattern is proposed. At first, thinning was performed before extracting features. End-point, inflexion-point, branch-point and cross-point were extracted as features. Using these features and the angular relations between these features, local graph pattern was made. When local graph pattern is made, the of strokes is investigated on contacting point. From this process, pattern is simplified as contacting pattern of the basic form and the contacting form we must take into account can be restricted within fixed region, 4therefore phoneme segmentation not influenced by characters form and any other contact in a single character is performed as matching this local graph pattern with base patterns searched ahead. This experiments with 540 characters have been conducted. From the result of this experiment, it is shown that phoneme segmentation is independent of characters form and other contact in a single character to obtain a correct segmentation rate of 95%, manages it efficiently to reduce the time spent in lock operation when the lock.

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Real-Time Vehicle License Plate Recognition System Using Adaptive Heuristic Segmentation Algorithm (적응 휴리스틱 분할 알고리즘을 이용한 실시간 차량 번호판 인식 시스템)

  • Jin, Moon Yong;Park, Jong Bin;Lee, Dong Suk;Park, Dong Sun
    • KIPS Transactions on Software and Data Engineering
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    • v.3 no.9
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    • pp.361-368
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    • 2014
  • The LPR(License plate recognition) system has been developed to efficient control for complex traffic environment and currently be used in many places. However, because of light, noise, background changes, environmental changes, damaged plate, it only works limited environment, so it is difficult to use in real-time. This paper presents a heuristic segmentation algorithm for robust to noise and illumination changes and introduce a real-time license plate recognition system using it. In first step, We detect the plate utilized Haar-like feature and Adaboost. This method is possible to rapid detection used integral image and cascade structure. Second step, we determine the type of license plate with adaptive histogram equalization, bilateral filtering for denoise and segment accurate character based on adaptive threshold, pixel projection and associated with the prior knowledge. The last step is character recognition that used histogram of oriented gradients (HOG) and multi-layer perceptron(MLP) for number recognition and support vector machine(SVM) for number and Korean character classifier respectively. The experimental results show license plate detection rate of 94.29%, license plate false alarm rate of 2.94%. In character segmentation method, character hit rate is 97.23% and character false alarm rate is 1.37%. And in character recognition, the average character recognition rate is 98.38%. Total average running time in our proposed method is 140ms. It is possible to be real-time system with efficiency and robustness.

Development of VIN Character Recognition System for Motor (자동차 VIN 문자 인식 시스템 개발)

  • 이용중;이화춘;류재엽
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2000.10a
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    • pp.68-73
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    • 2000
  • This study to embody automatic recognition of VIN(Vehicle Identification Number)character by computer vision system. Automatic recognition characters methods consist of the thining processing and the recognition of each character. VIN character and background classified using counting method of the size of connected pixels. Thining processing applied to segmentation of connected fundamental phonemes by Hilditch's algorithm. Each VIN character contours tracing algorithm used the Freeman's direction tracing algorithm.

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The Character Area Extraction and the Character Segmentation on the Color Document (칼라 문서에서 문자 영역 추출믹 문자분리)

  • 김의정
    • Journal of the Korean Institute of Intelligent Systems
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    • v.9 no.4
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    • pp.444-450
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    • 1999
  • This paper deals with several methods: the clustering method that uses k-means algorithm to abstract the area of characters on the image document and the distance function that suits for the HIS coordinate system to cluster the image. For the prepossessing step to recognize this, or the method of characters segmentate, the algorithm to abstract a discrete character is also proposed, using the linking picture element. This algorithm provides the feature that separates any character such as the touching or overlapped character. The methods of projecting and tracking the edge have so far been used to segment them. However, with the new method proposed here, the picture element extracts a discrete character with only one-time projection after abstracting the character string. it is possible to pull out it. dividing the area into the character and the rest (non-character). This has great significance in terms of processing color documents, not the simple binary image, and already received verification that it is more advanced than the previous document processing system.

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An Efficient Block Segmentation and Classification of a Document Image Using Edge Information (문서영상의 에지 정보를 이용한 효과적인 블록분할 및 유형분류)

  • 박창준;전준형;최형문
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.33B no.10
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    • pp.120-129
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    • 1996
  • This paper presents an efficient block segmentation and classification using the edge information of the document image. We extract four prominent features form the edge gradient and orientaton, all of which, and thereby the block clssifications, are insensitive to the background noise and the brightness variation of of the image. Using these four features, we can efficiently classify a document image into the seven categrories of blocks of small-size letters, large-size letters, tables, equations, flow-charts, graphs, and photographs, the first five of which are text blocks which are character-recognizable, and the last two are non-character blocks. By introducing the clumn interval and text line intervals of the document in the determination of th erun length of CRLA (constrained run length algorithm), we can obtain an efficient block segmentation with reduced memory size. The simulation results show that the proposed algorithm can rigidly segment and classify the blocks of the documents into the above mentioned seven categories and classification performance is high enough for all the categories except for the graphs with too much variations.

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Segmentation Algorithm for Wafer ID using Active Multiple Templates Model

  • Ahn, In-Mo;Kang, Dong-Joong;Chung, Yoon-Tack
    • 제어로봇시스템학회:학술대회논문집
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    • 2003.10a
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    • pp.839-844
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    • 2003
  • This paper presents a method to segment wafer ID marks on poor quality images under uncontrolled lighting conditions of the semiconductor process. The active multiple templates matching method is suggested to search ID areas on wafers and segment them into meaningful regions and it would have been impossible to recognize characters using general OCR algorithms. This active template model is designed by applying a snake model that is used for active contour tracking. Active multiple template model searches character areas and segments them into single characters optimally, tracking each character that can vary in a flexible manner according to string configurations. Applying active multiple templates, the optimization of the snake energy is done using Greedy algorithm, to maximize its efficiency by automatically controlling each template gap. These vary according to the configuration of character string. Experimental results using wafer images from real FA environment are presented.

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A Method for Caption Segmentation using Minimum Spanning Tree

  • Chun, Byung-Tae;Kim, Kyuheon;Lee, Jae-Yeon
    • Proceedings of the IEEK Conference
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    • 2000.07b
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    • pp.906-909
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    • 2000
  • Conventional caption extraction methods use the difference between frames or color segmentation methods from the whole image. Because these methods depend heavily on heuristics, we should have a priori knowledge of the captions to be extracted. Also they are difficult to implement. In this paper, we propose a method that uses little heuristics and simplified algorithm. We use topographical features of characters to extract the character points and use KMST(Kruskal minimum spanning tree) to extract the candidate regions for captions. Character regions are determined by testing several conditions and verifying those candidate regions. Experimental results show that the candidate region extraction rate is 100%, and the character region extraction rate is 98.2%. And then we can see the results that caption area in complex images is well extracted.

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