• Title, Summary, Keyword: Character recognition

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Character Segmentation and Recognition Algorithm for Various Text Region Images (다양한 문자열영상의 개별문자분리 및 인식 알고리즘)

  • Koo, Keun-Hwi;Choi, Sung-Hoo;Yun, Jong-Pil;Choi, Jong-Hyun;Kim, Sang-Woo
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.58 no.4
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    • pp.806-816
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    • 2009
  • Character recognition system consists of four step; text localization, text segmentation, character segmentation, and recognition. The character segmentation is very important and difficult because of noise, illumination, and so on. For high recognition rates of the system, it is necessary to take good performance of character segmentation algorithm. Many algorithms for character segmentation have been developed up to now, and many people have been recently making researches in segmentation of touching or overlapping character. Most of algorithms cannot apply to the text regions of management number marked on the slab in steel image, because the text regions are irregular such as touching character by strong illumination and by trouble of nozzle in marking machine, and loss of character. It is difficult to gain high success rate in various cases. This paper describes a new algorithm of character segmentation to recognize slab management number marked on the slab in the steel image. It is very important that pre-processing step is to convert gray image to binary image without loss of character and touching character. In this binary image, non-touching characters are simply separated by using vertical projection profile. For separating touching characters, after we use combined profile to find candidate points of boundary, decide real character boundary by using method based on recognition. In recognition step, we remove noise of character images, then recognize respective character images. In this paper, the proposed algorithm is effective for character segmentation and recognition of various text regions on the slab in steel image.

Low-Quality Banknote Serial Number Recognition Based on Deep Neural Network

  • Jang, Unsoo;Suh, Kun Ha;Lee, Eui Chul
    • Journal of Information Processing Systems
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    • v.16 no.1
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    • pp.224-237
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    • 2020
  • Recognition of banknote serial number is one of the important functions for intelligent banknote counter implementation and can be used for various purposes. However, the previous character recognition method is limited to use due to the font type of the banknote serial number, the variation problem by the solid status, and the recognition speed issue. In this paper, we propose an aspect ratio based character region segmentation and a convolutional neural network (CNN) based banknote serial number recognition method. In order to detect the character region, the character area is determined based on the aspect ratio of each character in the serial number candidate area after the banknote area detection and de-skewing process is performed. Then, we designed and compared four types of CNN models and determined the best model for serial number recognition. Experimental results showed that the recognition accuracy of each character was 99.85%. In addition, it was confirmed that the recognition performance is improved as a result of performing data augmentation. The banknote used in the experiment is Indian rupee, which is badly soiled and the font of characters is unusual, therefore it can be regarded to have good performance. Recognition speed was also enough to run in real time on a device that counts 800 banknotes per minute.

License Plate Recognition System Using Artificial Neural Networks

  • Turkyilmaz, Ibrahim;Kacan, Kirami
    • ETRI Journal
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    • v.39 no.2
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    • pp.163-172
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    • 2017
  • A high performance license plate recognition system (LPRS) is proposed in this work. The proposed LPRS is composed of the following three main stages: (i) plate region determination, (ii) character segmentation, and (iii) character recognition. During the plate region determination stage, the image is enhanced by image processing algorithms to increase system performance. The rectangular license plate region is obtained using edge-based image processing methods on the binarized image. With the help of skew correction, the plate region is prepared for the character segmentation stage. Characters are separated from each other using vertical projections on the plate region. Segmented characters are prepared for the character recognition stage by a thinning process. At the character recognition stage, a three-layer feedforward artificial neural network using a backpropagation learning algorithm is constructed and the characters are determined.

Lour-Quality Printed Character Recognition Considering of image Blurredness (찌그러짐을 고려한 저품질 인쇄체 문자인식)

  • 김성원;김형원;양윤모
    • Proceedings of the IEEK Conference
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    • pp.219-222
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    • 2000
  • Character recognition has already been studied in a lot of fields. But, if input-characters have noise in practical application system, the ability decreases markedly. Special consideration should be taken into account in the recognition of blurred data. This paper proposes low-quality printed character recognition methods that extracts blurred parts of the character image, deletes them and carry out accurate character recognition.

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Recognition of hand written hangeul based on the stroke order of the elementary segment

  • Song, Jeong-Young;Akizuki, Kageo;Lee, Hee-Hyol;Choi, Won-Kyu
    • 제어로봇시스템학회:학술대회논문집
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    • pp.302-306
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    • 1994
  • This paper describes how to recognize hand written Hangeul character using the stroke order of the elementary segment. The recognition system is constructed of parts : character input part, segment disassembling part, character element extraction part and character recognition part. The character input part reads the character and performs thinning algorithm. In the segment disassembling part, the input character is disassembled into elementary segments using the direction codes and the feature parameters. In the character element extraction part, we extract the character element using the stroke order and the knowledge rule. Finally, we able to recognize the hand written Hangeul characters by assembling the character elements, in the character recognition part.

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Feature Extraction Method for the Character Recognition of the Low Resolution Document

  • Kim, Dae-Hak;Cheong, Hyoung-Chul
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.3
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    • pp.525-533
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    • 2003
  • In this paper we introduce some existing preprocessing algorithm for character recognition and consider feature extraction method for the recognition of low resolution document. Image recognition of low resolution document including fax images can be frequently misclassified due to the blurring effect, slope effect, noise and so on. In order to overcome these difficulties in the character recognition we considered a mesh feature extraction and contour direction code feature. System for automatic character recognition were suggested.

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Character Recognition using Regional Structure

  • Yoo, Suk Won
    • International Journal of Advanced Culture Technology
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    • v.7 no.1
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    • pp.64-69
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    • 2019
  • With the advent of the fourth industry, the need for office automation with automatic character recognition capabilities is increasing day by day. Therefore, in this paper, we study a character recognition algorithm that effectively recognizes a new experimental data character by using learning data characters. The proposed algorithm computes the degree of similarity that the structural regions of learning data characters match the corresponding regions of the experimental data character. It has been confirmed that satisfactory results can be obtained by selecting the learning data character with the highest degree of similarity in the matching process as the final recognition result for a given experimental data character.

Research on Korea Text Recognition in Images Using Deep Learning (딥 러닝 기법을 활용한 이미지 내 한글 텍스트 인식에 관한 연구)

  • Sung, Sang-Ha;Lee, Kang-Bae;Park, Sung-Ho
    • Journal of the Korea Convergence Society
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    • v.11 no.6
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    • pp.1-6
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    • 2020
  • In this study, research on character recognition, which is one of the fields of computer vision, was conducted. Optical character recognition, which is one of the most widely used character recognition techniques, suffers from decreasing recognition rate if the recognition target deviates from a certain standard and format. Hence, this study aimed to address this limitation by applying deep learning techniques to character recognition. In addition, as most character recognition studies have been limited to English or number recognition, the recognition range has been expanded through additional data training on Korean text. As a result, this study derived a deep learning-based character recognition algorithm for Korean text recognition. The algorithm obtained a score of 0.841 on the 1-NED evaluation method, which is a similar result to that of English recognition. Further, based on the analysis of the results, major issues with Korean text recognition and possible future study tasks are introduced.

Simple Frame Marker: Implementation of In-Marker Image and Character Recognition and Tracking Method (심플 프레임 마커: 마커 내부 이미지 및 문자 패턴의 인식 및 추적 기법 구현)

  • Kim, Hye-Jin;Woo, Woon-Tack
    • 한국HCI학회:학술대회논문집
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    • pp.558-561
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    • 2009
  • In this paper, we propose Simple Frame Marker(SFMarker) to support recognition of characters and images included in a marker in augmented reality. If characters are inserted inside of marker and are recognised using Optical Character Recognition(OCR), it doesn't need marker learning process before an execution. It also reduces visual disturbance compared to 2D barcode marker due to familarity of characters. Therefore, proposed SFMarker distinguishes Square SFMarker that embeds images from Rectangle SFMarker with characters according to ratio of marker and applies different recognition algorithms. Also, in order to reduce preprocessing of character recognition, SFMarker inserts direction information in border of marker and extracts it to execute character recognition fast and correctly. Finally, since the character recognition for every frame slows down tracking speed, we increase the speed of recognition process using the result of character recognition in previous frame when frame difference is low.

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An On-line Speech and Character Combined Recognition System for Multimodal Interfaces (멀티모달 인터페이스를 위한 음성 및 문자 공용 인식시스템의 구현)

  • 석수영;김민정;김광수;정호열;정현열
    • Journal of Korea Multimedia Society
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    • v.6 no.2
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    • pp.216-223
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    • 2003
  • In this paper, we present SCCRS(Speech and Character Combined Recognition System) for speaker /writer independent. on-line multimodal interfaces. In general, it has been known that the CHMM(Continuous Hidden Markov Mode] ) is very useful method for speech recognition and on-line character recognition, respectively. In the proposed method, the same CHMM is applied to both speech and character recognition, so as to construct a combined system. For such a purpose, 115 CHMM having 3 states and 9 transitions are constructed using MLE(Maximum Likelihood Estimation) algorithm. Different features are extracted for speech and character recognition: MFCC(Mel Frequency Cepstrum Coefficient) Is used for speech in the preprocessing, while position parameter is utilized for cursive character At recognition step, the proposed SCCRS employs OPDP (One Pass Dynamic Programming), so as to be a practical combined recognition system. Experimental results show that the recognition rates for voice phoneme, voice word, cursive character grapheme, and cursive character word are 51.65%, 88.6%, 85.3%, and 85.6%, respectively, when not using any language models. It demonstrates the efficiency of the proposed system.

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