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Journal articles on the topic 'Image-character'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Barbar, Aziz, and Anis Ismail. "Character Image Semantic-Based CAPTCHA." International Journal of Future Computer and Communication 4, no. 3 (2015): 211–15. http://dx.doi.org/10.7763/ijfcc.2015.v4.387.

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2

Bian, Liheng, Huayi Wang, Chunli Zhu, and Jun Zhang. "Image-free multi-character recognition." Optics Letters 47, no. 6 (2022): 1343. http://dx.doi.org/10.1364/ol.451777.

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Wibawa, Condro, and Dessy Tri Anggraeni. "COMPARISON OF IMAGE SEGMENTATION METHOD IN IMAGE CHARACTER EXTRACTION PREPROCESSING USING OPTICAL CHARACTER RECOGINITON." Jurnal Teknik Informatika (Jutif) 4, no. 3 (2023): 583–89. http://dx.doi.org/10.52436/1.jutif.2023.4.3.956.

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Today, there are many documents in the form of digital images obtained from various sources which must be able to be processed by a computer automatically. One of the document image processing is text feature extraction using OCR (Optical Character Recognition) technology. However, in many cases OCR technology are unable to read text characters in digital images accurately. This could be due to several factor such as poor image quality or noise. In order to get accurate result, the image must be in a good quality, so that digital image need to be preprocessed. The image preprocessing method us

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S., Priyadharshini. "Image Text to Speech Conversion Using Optical Character Recognition." International Journal of Psychosocial Rehabilitation 24, no. 5 (2020): 4199–205. http://dx.doi.org/10.37200/ijpr/v24i5/pr2020134.

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5

Hu, Yuan Chun, Jian Sun, and Wei Liu. "Classification-Based Character Segmentation of Image." Applied Mechanics and Materials 519-520 (February 2014): 572–76. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.572.

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In traditional way, the segmentation of image is conducted by simple technology of image processing, which cannot be operated automatically. In this paper, we present a kind of classification method to find the boundary area to segment character image. Referring to sample points and sample areas, the essential segmentation information is extracted. By merging different formats of image transformation, including rotation, erosion and dilation, more features are used to train and test the segmentation model. Parameter tuning is also proposed to optimize the model for promotion. By the means of c

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6

Jana, Ranjan, Amrita Roy Chowdhury, and Mazharul Islam. "Optical Character Recognition from Text Image." International Journal of Computer Applications Technology and Research 3, no. 4 (2014): 240–44. http://dx.doi.org/10.7753/ijcatr0304.1009.

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7

Shao, Yunxue, Chunheng Wang, and Baihua Xiao. "A character image restoration method for unconstrained handwritten Chinese character recognition." International Journal on Document Analysis and Recognition (IJDAR) 18, no. 1 (2014): 73–86. http://dx.doi.org/10.1007/s10032-014-0232-9.

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8

Liu, Lin, and Jiaxin Wang. "Research on Chinese-character Image Streaming based on the Digital-Streaming Media Technology." E3S Web of Conferences 236 (2021): 05017. http://dx.doi.org/10.1051/e3sconf/202123605017.

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The survival mode of the font design has been changed to the mode of imaging design by the new-media technology. The Chinese character image could achieve different image effects of fonts including flow, page turning, rotation, and scrolling. Streaming media technology achieve the streaming playing approach to discuss the composition and dynamic effect of Chinese character image streaming. Based on the characteristics of digital-streaming media, particle and fluid technology, and Chinese character image streaming, this paper will analyze and sort out the formation and techniques of Chinese cha

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Vladimir, BRAJUC. "THE ARTISTIC STRUCTURE OF OBLOMOV'S IMAGE." Limbaj si context / Speech and Context International Journal of Linguistics, Semiotics and Literary Science 2/2018, no. 10 (2023): 27–54. https://doi.org/10.5281/zenodo.7707643.

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<em>The article examines the artistic structure of the image of Oblomov, the protagonist of the novel "Oblomov" by Ivan Gončarov. The dominant artistic method of creating the image in Gončarov’s novel — antithesis — is considered. </em>

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Zheng, En Xing, and Jing Tang. "Design and Realization of Color Character Overlay Based on AT89S51 and MC1377." Applied Mechanics and Materials 29-32 (August 2010): 809–13. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.809.

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This paper describes a color character overlay using color synchrolock and coding circuit composed of color television integrated circuit AT89S51and MC1377, introduces the basic method of overlay color character on television image, discusses relevant theory from microcomputer video image and color character overlay, puts forward a new character overlay method, and gives the video image character overlay’s design method based on AT89S51.

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Darma, I. Wayan Agus Surya, I. K. G. Darma Putra, and Made Sudarma. "EKSTRAKSI FITUR AKSARA BALI MENGGUNAKAN METODE ZONING." Majalah Ilmiah Teknologi Elektro 14, no. 2 (2015): 44. http://dx.doi.org/10.24843/mite.2015.v14i02p09.

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Feature extraction is an important process in character recognition system. The purpose of this process is to obtain special feature from a character image. This paper is focuses on how to obtain special feature from a handwritten Balinese character image using zoning. This algorithm dividing Balinese character image into multiple regions, then a special feature on each region resulting the data extracted feature. The test result in this paper generates a various semantic and direction feature data. This is because this paper using handwritten Balinese character. Furthermore, the features that

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Setyanto, Daniar Wikan, Santosa Soewarlan, and Sumbo Tinarbuko. "Words Representation of Postfeminism in Indonesian Heroine Character : Case Study of Valentine Film." Dewa Ruci: Jurnal Pengkajian dan Penciptaan Seni 14, no. 2 (2020): 47–56. http://dx.doi.org/10.33153/dewaruci.v14i2.2797.

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The heroine is a character who has succeeded in embracing the public imagination in terms of self-image and became the ideal image of female, including in Indonesia. The character of Srimaya/ Valentine is a heroine character coming from local comic taken into Indonesia’s movies. The image presented on Srimaya/ Valentine is the symptom of capitalism in the Indonesian’s movies, the character is also one of the case studies in image reconstruction product or the representation of female using their image as a heroine. The discourse of female representation in the character of Srimaya/ Valentine d

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13

Zhao, Hui Huang, De Jian Zhou, and Yu Ming Xu. "Research of the SMT Product Character Segmentation Based on Contour Feature." Advanced Materials Research 201-203 (February 2011): 2019–22. http://dx.doi.org/10.4028/www.scientific.net/amr.201-203.2019.

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The principles of the Surface Mount Technology (SMT) product character segmentation and its technology could be described as following: SMT product character image is obtained by image sampling equipment and its ideal binary images is got after image processing. In order to segment the SMT product character effectively, a novel character segmentation algorithm is proposed based on contour feature. Three kinds of information are extracted, one is the up contour feature, another is the under contour feature, the third is the width and the height of the image. Then the position of character segme

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14

R, Prithvi Raj, Rohith M, Ravichandra A R, and Shafien Ulla Khan. "IMAGE CHARACTER RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS." International Research Journal of Computer Science 9, no. 8 (2022): 304–11. http://dx.doi.org/10.26562/irjcs.2022.v0908.29.

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Object Recognition is a challenging and exciting task of computer Vision. Object recognition is to describe a collection of related computer vision tasks which involves identifying objects in images. Image classification involves predicting the class of objects in an image. Object localization refers to identifying the location of objects in an image. Image Annotation in Machine Learning is the process of creating bounding boxes around the localized images. Now with the advance of deep learning and neural networks, we can finally tackle these problems without coming up with various heuristics

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Gao, Feng, Jingping Zhang, Yongge Liu, and Yahong Han. "Image Translation for Oracle Bone Character Interpretation." Symmetry 14, no. 4 (2022): 743. http://dx.doi.org/10.3390/sym14040743.

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The Oracle Bone Characters are the earliest known ancient Chinese characters and are an important record of the civilization of ancient China.The interpretation of the Oracle Bone Characters is challenging and requires professional knowledge from ancient Chinese language experts. Although some works have utilized deep learning to perform image detection and recognition using the Oracle Bone Characters, these methods have proven difficult to use for the interpretation of uninterpreted Oracle Bone Character images. Inspired by the prior knowledge that there exists a relation between glyphs from

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16

Zhao, Yu-qing, Xiao-bo Yan, and Lin Wang. "Ballot character recognition based on image processing." Journal of Physics: Conference Series 2024, no. 1 (2021): 012010. http://dx.doi.org/10.1088/1742-6596/2024/1/012010.

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17

Watanabe, Kengo, Kazuya Ito, Kunio Kondo, and Shinichiro Miyaoka. "Character Collage System using Poisson Image Editing." Journal of the Society for Art and Science 9, no. 2 (2010): 58–65. http://dx.doi.org/10.3756/artsci.9.58.

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18

Laundy, Peter, and Susan Thornton Rogers. "Don't Image Your POSITION, Express Your CHARACTER." Design Management Journal (Former Series) 6, no. 1 (2010): 9–16. http://dx.doi.org/10.1111/j.1948-7169.1995.tb00417.x.

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19

Shi, Zhixin, and Venu Govindaraju. "Character image enhancement by selective region-growing." Pattern Recognition Letters 17, no. 5 (1996): 523–27. http://dx.doi.org/10.1016/0167-8655(95)00126-3.

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20

Jyoti, Dalal*1 &. Sumiran Daiya2. "IMAGE PROCESSING BASED OPTICAL CHARACTER RECOGNITION USING MATLAB." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 7, no. 5 (2018): 406–11. https://doi.org/10.5281/zenodo.1247281.

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Character recognition techniques associate a symbolic identity with the image of character. In a typical OCR systems input characters are digitized by an optical scanner. Each character is then located and segmented, and the resulting character image is fed into a pre-processor for noise reduction and normalization. Certain characteristics are the extracted from the character for classification. The feature extraction is critical and many different techniques exist, each having its strengths and weaknesses. After classification the identified characters are grouped to reconstruct the original

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21

Reynaldo, Kevin, and Imam Salehudin. "Influence of Character Image to Word-of-Mouth Through Gamer Satisfaction of Impulse Spending in the Genshin Impact Mobile Game." Journal of Games, Game Art, and Gamification 9, no. 1 (2024): 1–10. http://dx.doi.org/10.21512/jggag.v9i1.10538.

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This research aims to understand the influence of character image (symbolic representation and self-image congruence) on impulse buying and satisfaction in word-of-mouth recommendations for the game Genshin Impact in Indonesia. Hence, the study explored the potential of character image by spreading online survey to gen Z Genshin Impact gamers in Indonesia. The hypotheses in this study were tested using Structural Equation Modelling (SEM) with Lisrel 8.8. The significant results of this study to show character image have positive influence between impulse buying, satisfaction, and word-of-mouth

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С. В. ВОЛКОВА and А. В. ЛУЧИНСЬКА. "TYPES OF TRANSFORMATIONS IN UKRAINIAN TRANSLATION OF J.K. ROWLING'S “HARRY POTTER AND THE PHILOSOPHER'S STONE”: LINGUOSEMIOTIC ASPECT." MESSENGER of Kyiv National Linguistic University. Series Philology 22, no. 2 (2019): 202–14. http://dx.doi.org/10.32589/2311-0821.2.2019.192007.

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Introduction. The study focuses on the translation transformations in reproducing the linguosemiotics of the image of the character in Ukrainian translation of “Harry Potter and the Philosopher’s Stone” by J. K. Rowling. The understanding of such concepts as “image”, “literary image”, and “character image” in linguistics and translation studies’ works is specified.The paper analyzes the translation studies investigations of the text of “Harry Potter and the Philosopher’s Stone”. The literary image of Harry Potter, the main character of “Harry Potter and the Philosopher’s Stone”, is considered

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23

Mykhailova, Rada, and Yevgeniya Fedorova. "About Contents and Correlation of Concepts «Character» and «Image»." Bulletin of KNUKiM. Series in Arts, no. 35 (December 1, 2016): 206–17. https://doi.org/10.31866/2410-1176.35.2016.158260.

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Main objective of the study. Article research of maintenance of concepts «character» and «image», that have deployment in the spheres of design, industry of fashion, stilies, which usually apply as identical as double expression «image-character». Research methodology consists of application of complex analysis of formulations and determinations of every concept in the context of different disciplines, straight or indirect related to this main. The scientific novelty of work consists of clarification of ideas about contents terms «image» and &laq

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24

Chen, Tianyu. "A Method for Training-free Person Image Picture Generation." Highlights in Science, Engineering and Technology 57 (July 11, 2023): 168–75. http://dx.doi.org/10.54097/hset.v57i.9996.

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The current state-of-the-art Diffusion model has demonstrated excellent results in generating images. However, the images are monotonous and are mostly the result of the distribution of images of people in the training set, making it challenging to generate multiple images for a fixed number of individuals. This problem can often only be solved by fine-tuning the training of the model. This means that each individual/animated character image must be trained if it is to be drawn, and the hardware and cost of this training is often beyond the reach of the average user, who accounts for the large

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25

Jiang, Wei. "The Application of Image Recognition Based on BP Neural Networks in Automatic Steel Rolling." Advanced Materials Research 764 (September 2013): 161–64. http://dx.doi.org/10.4028/www.scientific.net/amr.764.161.

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A BP neural networks is presented for billet character recognition. Firstly, by a series of image processing techniques, the character’feature in the billet character region of the video image gathered by frame grabber is abstracted. Secondly, the BP neural networks algorithm is employed for character recognition. Application results show that the image recognition based BP neural networks can performs well in billet character recognition, and the method presented is speedy, efficient and of high value in practice.

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26

Yeroshkina, Y., and A. Ivanov. "FEATURES OF THE FORMATION OF THE ARTISTIC IMAGE OF THE MODEL AND SYMBOL IN ART." Municipal economy of cities 1, no. 154 (2020): 199–203. http://dx.doi.org/10.33042/2522-1809-2020-1-154-199-203.

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In the article an image, his structure and value, is investigated in an art. The role of model and symbol is considered as the special form of reflection of reality. By a comparative analysis essence of image, model and symbol is educed. Problem aspects are considered, features rational and emotional, general and single in an image. Certain character in an art, that Simultaneously is image as method, method of reflection, is educed, there is both material and ideal. An image is, concept that determines the most wide phenomenon in an art, namely ‒ a method, method of reflection, that marks off

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27

Liu, Lin, and Wenjun Jiang. "Study On The Graphic Structure And The Communication Ways Of Chinese Characters In The Light Of New Media." E3S Web of Conferences 236 (2021): 05064. http://dx.doi.org/10.1051/e3sconf/202123605064.

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Objective The new media has promoted the presentation of three-dimensional dynamic Chinese character images on the screen, with temporal and spacial, virtual and interactive characteristics. Therefore, this article aims to study the influence of new media art on the graphic structure and the communication ways of Chinese characters. Method Through the Exploration of the graphic structure of Chinese character and screen, visual representations of the character image and communication ways of the image, this article makes it clear that the character image supported by the new media technology is

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Jia, Zhiwei, Shugong Xu, Shiyi Mu, and Yue Tao. "Learning-Based Text Image Quality Assessment with Texture Feature and Embedding Robustness." Electronics 11, no. 10 (2022): 1611. http://dx.doi.org/10.3390/electronics11101611.

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The quality of the input text image has a clear impact on the output of a scene text recognition (STR) system; however, due to the fact that the main content of a text image is a sequence of characters containing semantic information, how to effectively assess text image quality remains a research challenge. Text image quality assessment (TIQA) can help in picking a hard sample, leading to a more robust STR system and recognition-oriented text image restoration. In this paper, by arguing that the text image quality comes from character-level texture feature and embedding robustness, we propose

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W. B. Yu , T. T. Ma. "A study on offline handwritten Chinese character recognition based on chaotic iteration." Mustansiriyah Journal of Pure and Applied Sciences 2, no. 1 (2024): 62–75. http://dx.doi.org/10.47831/mjpas.v2i1.100.

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Offline handwritten Chinese character recognition technology has important practical value in the field of automatic machine entryomit, and is one of the research hotspots of intelligent technology. In this paper, a handwritten Chinese character feature extraction method based on dynamic system iteration is constructed by using chaos theory. Firstly, from the perspective of system iteration, the Chinese character image is preprocessed to optimize the image information. Secondly, the iterative matrix constructed by the auxiliary function is combined with the handwritten Chinese character image

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Boboqulova, Oyjamol Saidovna. "LEADING CHARACTERISTICS IN THE IMAGE OF RIND AND IN IMAGE RINDONA CHARACTER." Theoretical & Applied Science 145, no. 05 (2025): 355–59. https://doi.org/10.15863/tas.2025.05.145.42.

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31

Liu, Ying Jie, and Fu Cheng You. "Application of Mathematical Morphology on Touching or Broken Characters Processing." Advanced Materials Research 171-172 (December 2010): 73–77. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.73.

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It is difficult to process touching or broken characters in practical applications on optical character recognition. For touching or broken characters, a method based on mathematical morphology of binary image is put forward in the paper. On the basis of the relative theories of digital image processing, the overall process is introduced including separation of touching characters and connection of broken characters. First of all, character image is pre-processed through smoothing and threshold segmentation in order to generate binary image of characters. Then character regions which are touch

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32

Gunawan, Hendy, Janson Hendryli, and Dyah Erny Herwindiati. "Program Konversi Citra Notasi Balok Menjadi Notasi Angka." Computatio : Journal of Computer Science and Information Systems 2, no. 2 (2018): 119. http://dx.doi.org/10.24912/computatio.v2i2.2278.

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The Image Conversion Program of Music Notation being Numeric Notation is a character recognition system that accepts input in form of music notation image that produces an output of a DOCX file containing the numeric notation from the input image. Music notation has notation value, ritmic value and written with a music stave. The system consists of four main processes: preprocessing (grayscale and thresholding), notation line segmentation, notation character segmentation, and template matching. Template matching is used to recognize the music notation that obtained after segmentation. The reco

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33

Cui, Tianwei. "Research on Identification System of Aero Engine Blade." Innovation in Science and Technology 1, no. 3 (2022): 86–96. http://dx.doi.org/10.56397/ist.2022.10.08.

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This paper mainly focuses on the recognition of engraved code information, including three steps: character image acquisition, character image preprocessing and character recognition. To carry out the research of blade identity information integration technology, and realize the end-to-end identification of blade identity information.

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34

Yun, Seihee. "A Study on Character Implementation and Image Expression Characteristics in <Why Women Kill Season I>." Korean Society of Culture and Convergence 45, no. 7 (2023): 247–59. http://dx.doi.org/10.33645/cnc.2023.07.45.07.247.

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The purpose of this paper is to discuss the image expression characteristics according to the character implementation elements of the three heroines in the drama <Why Women Kill season I>. The elements of character implementation are 1) environmental factors of the times, 2) event factors, and 3) personality factors of characters. The background of the play is the 1960s, 1980s, and 2019, and the female image of each era is expressed using costumes, makeup, and accessories. In addition, it was found that the major events experienced by the character cause psychological changes, which aff

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M.L.S.N.S, Lakshmi, Jones Christydass Prasad, and cheerla Sreevardhan. "License Plate Detection using Optical Character Recognition." International Journal of Applied Engineering Research, no. 5 (December 30, 2020): 2. https://doi.org/10.5281/zenodo.7550213.

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A unique use of optical character recognition is automatic license plate detection. Using digital photos, a car can be recognized from its license plate using image processing technologies. In this paper, we present a method for optical character recognition-based license plate detection (OCR). OCR, which is an optically processed character on digital images, is used to identify text from a digital image. Tests of this method are conducted using various photos and lighting setups. To identify the real characters on the license plate, we have employed image process

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36

Khashimova, Sh. "The artistic interpretation of women’s image and character." ACADEMICIA: An International Multidisciplinary Research Journal 10, no. 12 (2020): 1220–23. http://dx.doi.org/10.5958/2249-7137.2020.01885.6.

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37

XU Zhuo, 许. 卓., 金熙哲 JIN Xi-zhe, 吴海龙 WU Hai-long, et al. "Influence of TFT character on LCD image sticking." Chinese Journal of Liquid Crystals and Displays 33, no. 3 (2018): 195–201. http://dx.doi.org/10.3788/yjyxs20183303.0195.

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Rajan, G. High Court, S. Pandiaraj, and Dr J. Jagadeesan. "Optical Character Recognition using Dynamic Memory Image Algorithm." IOSR Journal of Computer Engineering 16, no. 2 (2014): 12–16. http://dx.doi.org/10.9790/0661-16271216.

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Choi, Jong-In, Soo-Kyun Kim, and Shin-Jin Kang. "Image Translation Method for Game Character Sprite Drawing." Computer Modeling in Engineering & Sciences 131, no. 2 (2022): 747–62. http://dx.doi.org/10.32604/cmes.2022.018201.

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Butler, Clare, Jocelyn Finniear, Anne Marie Doherty, and Steve Hill. "Exploring identity: a figurative character image-elicitation approach." Qualitative Research in Organizations and Management: An International Journal 9, no. 2 (2014): 151–68. http://dx.doi.org/10.1108/qrom-10-2012-1103.

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Purpose – The purpose of this paper is to explore the potential of incorporating visual methods in the study of identity and identity work. Design/methodology/approach – Scholars have proposed a range of approaches to the study of identity. However, studies have typically relied on interviews or surveys with little exploration of the dynamic narrating of self-in-situ inherent to identity and identity work. The paper reviews the aforementioned methods, builds on the power of visual approaches, and proposes a method involving figurative character image-elicitation (FCI). FCI uses personal, conte

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Yu, Wanbo, Yaosheng Li, Hongwei Peng, and Li Zhang. "Image iterative method for handwritten Chinese character recognition." Journal of Physics: Conference Series 1684 (November 2020): 012101. http://dx.doi.org/10.1088/1742-6596/1684/1/012101.

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42

Hooper, Keith, and Gina Xu. "From legitimacy by character to legitimacy by image." Managerial Auditing Journal 27, no. 8 (2012): 754–73. http://dx.doi.org/10.1108/02686901211257037.

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SAWADA, Takeshi, and Makoto OYA. "1016 OFF-line character recognition by image processing." Proceedings of Conference of Hokuriku-Shinetsu Branch 2010.47 (2010): 377–78. http://dx.doi.org/10.1299/jsmehs.2010.47.377.

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Choi, Woo-Seok. "The Proportion of Game Character and Game Image." Journal of Digital Convergence 14, no. 12 (2016): 165–72. http://dx.doi.org/10.14400/jdc.2016.14.12.165.

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Yuan, Wang Yuan, Zhang Cheng Ming, and Liang Yong. "An image super-resolution method considering edge character." IOP Conference Series: Earth and Environmental Science 17 (March 18, 2014): 012179. http://dx.doi.org/10.1088/1755-1315/17/1/012179.

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Li, Fangyi, Qiang Shen, Ying Li, and Neil Mac Parthaláin. "Handwritten Chinese character recognition using fuzzy image alignment." Soft Computing 20, no. 8 (2015): 2939–49. http://dx.doi.org/10.1007/s00500-015-1923-y.

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Alotaibi, Faiz, Muhamad Taufik Abdullah, Rusli Bin Hj Abdullah, Rahmita Wirza Binti O. K. Rahmat, Ibrahim Abaker Targio Hashem, and Arun Kumar Sangaiah. "Optical Character Recognition for Quranic Image Similarity Matching." IEEE Access 6 (2018): 554–62. http://dx.doi.org/10.1109/access.2017.2771621.

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Akkala, Abhilasha, and Avinash Seekoli. "TEXTRACTOR-EXTRACT OPTIMAL CHARACTER IN AN IMAGE FILE." Far East Journal of Electronics and Communications 21, no. 1-2 (2019): 21–26. http://dx.doi.org/10.17654/ec021120021.

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Jin, Ruichen, and Jongweon Kim. "3D CHARACTER MODEL RECOGNITION METHOD USING CARTOON IMAGE." JP Journal of Heat and Mass Transfer, Special (November 1, 2019): 145–52. http://dx.doi.org/10.17654/hmsi119145.

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Anggraeny, Fetty Tri, Yisti Vita Via, and Retno Mumpuni. "Image preprocessing analysis in handwritten Javanese character recognition." Bulletin of Electrical Engineering and Informatics 12, no. 2 (2023): 860–67. http://dx.doi.org/10.11591/eei.v12i2.4172.

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Abstract:

The handwriting produced by each person is unique, so each person has a different stroke, even though they write the same letter. Handwritten Javanese is an exciting topic to study, in addition to scientific purposes and preserving Indonesian culture. The Javanese character image dataset is aksara Jawa: aksara Jawa custom dataset from the Kaggle database consists of 2,154 train data and 480 evaluation data. This research proposed to analyze the impact of some preprocessing methods in recognizing handwritten Javanese characters. The preprocessing methods are dilation, skeletonization, and noise

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