Relevant bibliographies by topics / Image-character

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Image-character'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Image-character"

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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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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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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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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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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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 &quot;Oblomov&quot; by Ivan Gončarov. The dominant artistic method of creating the image in Gončarov&rsquo;s novel &mdash; antithesis &mdash; 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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Dissertations / Theses on the topic "Image-character"

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Lundqvist, Filip, and Olle Wallberg. "Natural image distortions and optical character recognition accuracy." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187234.

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Current state of the art optical character recognition tools are trained using high quality image datasets. In practical applications, natural images used for character recognition willnot always be of high quality. This report examines the accuracy of a state of the art optical character recognition tool using three distorted natural image datasets. The performed distortions were lossy JPEG compression, contrast reduction and white gaussian noise injection. The accuracy is presented as an average percentage of correct and located text using the Levenshtein distance algorithm. The results indi
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遲秉壯 and Ping-chong Chee. "Hand-printed Chinese character recognition and image preprocessing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1996. http://hub.hku.hk/bib/B31213972.

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Chee, Ping-chong. "Hand-printed Chinese character recognition and image preprocessing /." Hong Kong : University of Hong Kong, 1996. http://sunzi.lib.hku.hk/hkuto/record.jsp?B18597579.

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Leatherbury, Sean Villareal. "Inscribed within the image : the visual character of early Christian mosaic inscriptions." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:9ea6f425-7010-4820-b35d-bed33c658b60.

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Between the fourth and the seventh centuries CE, Christian patrons erected thousands of churches, chapels, and monasteries in cities and villages across the Mediterranean, decorating the apses, walls and floors of many of these structures with figural and geometric mosaics. These late antique Christian mosaics have been studied for their iconography, their Graeco-Roman components, and as evidence for the religious beliefs of newly-Christian patrons. However, art historians largely have ignored the ways that texts, inscribed within the visual field and composed of the same mosaic material, func
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Aula, Lara. "Improvement of Optical Character Recognition on Scanned Historical Documents Using Image Processing." Thesis, Högskolan i Gävle, Datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-36244.

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As an effort to improve accessibility to historical documents, digitization of historical archives has been an ongoing process at many institutions since the origination of Optical Character Recognition. The old, scanned documents can contain deteriorations acquired over time or caused by old printing methods. Common visual attributes seen on the documents are variations in style and font, broken characters, ink intensity, noise levels and damage caused by folding or ripping and more. Many of these attributes are disfavoring for modern Optical Character Recognition tools and can lead to failed
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Kozlovski, Nikolai. "TEXT-IMAGE RESTORATION AND TEXT ALIGNMENT FOR MULTI-ENGINE OPTICAL CHARACTER RECOGNITION SYSTEMS." Master's thesis, University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3607.

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Previous research showed that combining three different optical character recognition (OCR) engines (ExperVision&reg; OCR, Scansoft OCR, and Abbyy&reg; OCR) results using voting algorithms will get higher accuracy rate than each of the engines individually. While a voting algorithm has been realized, several aspects to automate and improve the accuracy rate needed further research. This thesis will focus on morphological image preprocessing and morphological text restoration that goes to OCR engines. This method is similar to the one used in restoration partial finger prints. Series of morphol
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Nahar, Vikas. "Content based image retrieval for bio-medical images." Diss., Rolla, Mo. : Missouri University of Science and Technology, 2010. http://scholarsmine.mst.edu/thesis/pdf/Nahar_09007dcc80721e0b.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2010.<br>Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed Dec. 23, 2009). Includes bibliographical references (p. 82-83).
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Walvoord, Derek J. "Advanced correlation-based character recognition applied to the Archimedes Palimpsest /." Online version of thesis, 2008. http://hdl.handle.net/1850/7064.

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Kirkpatrick, Michael Gorden. "Optical character recognition : an approach using self- adjusting segmentation of a matrix." Virtual Press, 1997. http://liblink.bsu.edu/uhtbin/catkey/1048390.

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The problem of optical pattern recognition is a broad one. It ranges from identifying shapes in aerial photographs to recognizing letters in hand or machine printed words. This thesis examines many of the issues relating to pattern recognition and, specifically, those pertaining to the optical recognition of characters. It discusses several approaches to various parts of the problem as an illustration of the variety of methods of attack. Some of the particular strengths and weaknesses of those approaches are discussed as well. Finally, a new method of approaching OCR is introduced, developed,
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Kraljevic, Matija. "Character recognition in natural images : Testing the accuracy of OCR and potential improvement by image segmentation." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-187991.

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In recent years, reading text from natural images has gained renewed research attention. One of the main reasons for this is the rapid growth of camera-based applications on smart phones and other portable devices. With the increasing availability of high performance, low-priced, image-capturing devices, the application of scene text recognition is rapidly expanding and becoming increasingly popular. Despite many efforts, character recognition in natural images, is still considered a challenging and unresolved problem. The difficulties stem from the fact that natural images suffer from a wide
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Books on the topic "Image-character"

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McCarthy, Anne S. Image character recognition (ICR): An introduction. Association for Information and Image Management, 1994.

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Copyright Paperback Collection (Library of Congress), ed. Daily in your image: Reflecting the character of Christ. Barbour Pub., 2004.

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Lacey, D. I. Image and spirit of place: Conservation of character of sites and settlements. Oxford Brookes University, 1994.

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Reimel, J. Christopher. Image processing and optical character recognition: How they work and how to implement them. American Institute of Certified Public Accountants, 1993.

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Rossetti, Barbara. Optical scanning technology for purposes other than ballot counting. National Clearinghouse on Election Administration, Federal Election Commission, 1992.

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Horst, Bunke, Wang Patrick S-P, and Baird Henry S, eds. Document image analysis. World Scientific, 1994.

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1949-, Kasturi Rangachar, ed. Executive briefing: Document image analysis. IEEE Computer Society Press, 1997.

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Komotini), International Symposium for Thracian Studies (1st 1987. Byzantine Thrace: Image and character : first International Symposium for Thracian Studies, Komotini, May 28th - 31st, 1987. A. M. Hakkert, 1989.

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W, Klein William, and National Institute of Standards and Technology (U.S.), eds. Creating and validating a large image database for METTREC. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1997.

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A, Janet Stanley, Klein William W, and National Institute of Standards and Technology (U.S.), eds. Federal register document image database: NIST special database 25, volume 1. U.S. Dept. of Commerce, Technology Admininstration, National Institute of Standards and Technology, 1998.

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Book chapters on the topic "Image-character"

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Badarch, Zorig, Battogtokh Jigjidsuren, Nomin-Erdene Dalkhaa, and Ganbat Baasantseren. "CNN Character Recognition Model for 3D Integral Image Character." In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6420-2_2.

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Zhuang, Yueting, Xiafen Zhang, Jiangqin Wu, and Xiqun Lu. "Retrieval of Chinese Calligraphic Character Image." In Advances in Multimedia Information Processing - PCM 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30541-5_3.

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Zhang, Huaping, and Jianyun Shang. "Image Semantic Representation and Character Recognition." In Natural Language Processing and Applications. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9739-4_8.

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Chakraborty, Partha, Shanta Roy, Sadia Nowshin Sumaiya, and Aditi Sarker. "Handwritten Character Recognition from Image Using CNN." In Micro-Electronics and Telecommunication Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9512-5_4.

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Bharadwaj, Nithin Chandra, G. Praveen ., S. Naveen Kumar, and Sundari Tribhuvanam. "Airborne Character Recognition System with Image Processing Techniques." In Lecture Notes in Electrical Engineering. Springer India, 2013. http://dx.doi.org/10.1007/978-81-322-1157-0_68.

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Zhang, Xiafen, and Guangzhong Liu. "Chinese Calligraphy Character Image Synthesis Based on Retrieval." In Advances in Multimedia Information Processing - PCM 2009. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10467-1_14.

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Yu, Fei, Hunag Huang, Cheng Xu, Xiao-peng Dai, and Miao-liang Zhu. "Badness Information Audit Based on Image Character Filtering." In Parallel and Distributed Processing and Applications - ISPA 2005 Workshops. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11576259_70.

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Yu, Kai, Jiangqin Wu, and Yueting Zhuang. "Skeleton-Based Recognition of Chinese Calligraphic Character Image." In Advances in Multimedia Information Processing - PCM 2008. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89796-5_24.

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Gu, Kangzheng, Jiansong Bai, Qichen Zhang, Junjie Peng, and Wenqiang Zhang. "Image Generation for Printed Character by Representation Learning." In Advances in Multimedia Information Processing – PCM 2018. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00764-5_60.

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Jana, Ranjan, and Siddhartha Bhattacharyya. "Character Recognition from Handwritten Image Using Convolutional Neural Networks." In Recent Trends in Signal and Image Processing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6783-0_3.

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Conference papers on the topic "Image-character"

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Yoshizu, Yuya, Hayata KanekoRyuto Ishibashi, and Lin Meng. "Evaluation of Missing Image Restoration with a Binary Character Image Diffusion Model." In 2024 International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE, 2024. https://doi.org/10.1109/icamechs63130.2024.10818796.

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Yuan, Yuyi, and Bin Chen. "A Multimodal Incentive-Based Approach to Anime Character Image Design." In 2024 5th International Conference on Intelligent Design (ICID). IEEE, 2024. https://doi.org/10.1109/icid64166.2024.11024950.

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Weigang, Li, Rafael Marconi Ramos, and Pedro Carvalho Brom. "Threshold Determination for Chinese Character Image Processing in Multimodal Information Fusion." In 2024 International Conference on Asian Language Processing (IALP). IEEE, 2024. http://dx.doi.org/10.1109/ialp63756.2024.10661155.

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Belay, Birhanu Hailu, Tewodros Amberbir Habtegebrial, and Didier Stricker. "Amharic Character Image Recognition." In 2018 IEEE 18th International Conference on Communication Technology (ICCT). IEEE, 2018. http://dx.doi.org/10.1109/icct.2018.8599888.

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Elnabawy, Reham, Rimon Elias, and Mohammed Salem. "Image Based Hieroglyphic Character Recognition." In 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS). IEEE, 2018. http://dx.doi.org/10.1109/sitis.2018.00016.

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Liu, Tsann-Shyong, Bor-Shenn Jeng, Gan-How Chang, and Rong-Hauh Ju. "Image Coding For Chinese Character Patterns." In 1987 Cambridge Symposium, edited by T. Russell Hsing. SPIE, 1987. http://dx.doi.org/10.1117/12.976486.

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Uchida, Seiichi, Ryosuke Ishida, Akira Yoshida, Wenjie Cai, and Yaokai Feng. "Character Image Patterns as Big Data." In 2012 International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2012. http://dx.doi.org/10.1109/icfhr.2012.190.

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Lin, Yixuan. "Ancient Character Image Classification Model Training." In 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA). IEEE, 2022. http://dx.doi.org/10.1109/cvidliccea56201.2022.9824485.

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Newell, Andrew J., and Lewis D. Griffin. "Natural Image Character Recognition Using Oriented Basic Image Features." In 2011 International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2011. http://dx.doi.org/10.1109/dicta.2011.39.

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Wang, Ning. "Printed Chinese Character Recognition Based on Pixel Distribution Probability of Character Image." In 2008 Fourth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2008. http://dx.doi.org/10.1109/iih-msp.2008.118.

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Reports on the topic "Image-character"

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Grosso, Robert, and Margaret Pinson. COCRID: A Challenging Optical Character Recognition Image Dataset. Institute for Telecommunication Sciences, 2023. https://doi.org/10.70220/k3sdtbjc.

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Garris, Michael D., Stanley Janet, and William W. Klein. Impact of image quality on machine print optical character recognition. National Institute of Standards and Technology, 1997. http://dx.doi.org/10.6028/nist.ir.6101.

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Бережна, Маргарита Василівна. Maleficent: from the Matriarch to the Scorned Woman (Psycholinguistic Image). Baltija Publishing, 2021. http://dx.doi.org/10.31812/123456789/5766.

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The aim of the research is to identify the elements of the psycholinguistic image of the leading character in the dark fantasy adventure film Maleficent directed by Robert Stromberg (2014). The task consists of two stages, at the first of which I identify the psychological characteristics of the character to determine to which of the archetypes Maleficent belongs. As the basis, I take the classification of film archetypes by V. Schmidt. At the second stage, I distinguish the speech peculiarities of the character that reflex her psychological image. This paper explores 98 Maleficent’s turns of
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Бережна, Маргарита Василівна. The Destroyer Psycholinguistic Archetype. Baltija Publishing, 2021. http://dx.doi.org/10.31812/123456789/6036.

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The aim of the research is to identify the elements of the psycholinguistic image of the main antagonist Hela in the superhero film Thor: Ragnarok based on the Marvel Comics and directed by Taika Waititi (2017). The task consists of two stages, at the first of which I identify the psychological characteristics of the character to determine to which of the archetypes Hela belongs. As the basis, I take the classification of film archetypes by V. Schmidt. At the second stage, I distinguish the speech peculiarities of the character that reflect her psychological image.
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Poloboc, Alina. Fancy Lollipop. Intellectual Archive, 2023. http://dx.doi.org/10.32370/iaj.2997.

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"Fancy Lollipop" is a vibrant and energetic artwork featuring a blend of bold and bright colors. The color palette, which includes shades of blue, pink, and black, creates a sense of drama and theatricality in the piece. The colors are strategically placed in the composition to emphasize key elements of the image, such as the main character, Fancy Lollipop. Speaking of the main character, Fancy Lollipop is depicted as an extravagant and self-assured individual. Their presence in the artwork is unmistakable, and their confident and assured stance reflects their bold and attention-grabbing perso
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Бережна, Маргарита Василівна. The Traitor Psycholinguistic Archetype. Premier Publishing, 2022. http://dx.doi.org/10.31812/123456789/6051.

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Film studies have recently begun to employ Jung’s concept of archetypes prototypical characters which play the role of blueprint in constructing clear-cut characters. New typologies of archetype characters appear to reflect the changes in the constantly developing world of literature, theater, film, comics and other forms of entertainment. Among those, there is the classification of forty-five master characters by V. Schmidt , which is the basis for defining the character’s archetype in the present article. The aim of the research is to identify the elements of the psycholinguistic image of Ju
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Бережна, Маргарита Василівна. Psycholinguistic Image of Joy (in the Computer-Animated Film Inside Out). Psycholinguistics in a Modern World, 2021. http://dx.doi.org/10.31812/123456789/5827.

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The paper is focused on the correlation between the psychological archetype of a film character and the linguistic elements composing their speech. The Nurturer archetype is represented in the film Inside Out by the personalized emotion Joy. Joy is depicted as an antropomorphous female character, whose purpose is to keep her host, a young girl Riley, happy. As the Nurturer, Joy is completely focused on Riley’s happiness, which is expressed by lexico-semantic group ‘happy’, positive evaluative tokens, exclamatory sentences, promissive speech acts, and repetitions. She needs the feeling of conne
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Blais-Stevens, A., A. Castagner, A. Grenier, and K D Brewer. Preliminary results from a subbottom profiling survey of Seton Lake, British Columbia. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/332277.

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Seton Lake is a freshwater fiord located in southwestern British Columbia, roughly 4 km west of Lillooet and 250 km north-northeast of Vancouver. Located in the Coast Mountains, it is an alpine lake about 22-km long and roughly 1-1.5 km wide. It is separated from nearby Anderson Lake, located to the west, by a large pre-historic rock avalanche deposit at Seton Portage. The lake stands at about 243 m above sea level and is up to about 150 m deep (BC gov., 1953). Water level is controlled by a hydroelectric dam (i.e., Seton dam) located at the eastern end of the lake. Here, the lake drains east
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