Relevant bibliographies by topics / Handwriting-to-Text

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

Academic literature on the topic 'Handwriting-to-Text'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Handwriting-to-Text"

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Subash, R., K. Prasannavenkatesan та Dr R. Sunitharam. "Digital Text to Users Handwriting (தமிழ்)". International Journal of Soft Computing and Engineering 13, № 1 (2023): 10–15. http://dx.doi.org/10.35940/ijsce.a3588.0313123.

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Converting digital text to handwriting is a simple process because of the abundance of software and websites that do it, like texttohandwriting.com. The Text to Handwriting Converter is a free artificial intelligence-based tool that translates computer text into handwritten text with ease. An individual's handwriting format is saved as an input, converted into text, and then shown as an output. Image processing techniques can be used to process the handwriting. It is possible to use the alphabets of specific languages, such as Tamil (தமிழ்), English, etc. The text of the input is finally displ
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Dr., R. Sunitharam, Subash R. та Prasannavenkatesan K. "Digital Text to Users Handwriting (தமிழ்)". International Journal of Soft Computing and Engineering (IJSCE) 13, № 1 (2023): 10–15. https://doi.org/10.35940/ijsce.A3588.0313123.

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<strong>Abstract: </strong>Converting digital text to handwriting is a simple process because of the abundance of software and websites that do it, like texttohandwriting.com. The Text to Handwriting Converter is a free artificial intelligence-based tool that translates computer text into handwritten text with ease. An individual&#39;s handwriting format is saved as an input, converted into text, and then shown as an output. Image processing techniques can be used to process the handwriting. It is possible to use the alphabets of specific languages, such as Tamil (தமிழ்), English, etc. The tex
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Arya, Anil, A. Ashiq, M. S. Aswathy, and P. S. Akhila. "A Comparative Review of Different Techniques for Handwriting to Text Conversion." Advanced Innovations in Computer Programming Languages 7, no. 1 (2024): 1–9. https://doi.org/10.5281/zenodo.13766826.

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<em>Handwriting to text conversion, also known as handwriting recognition, is the process of converting handwritten text into machine-readable text. This article presents a comparative review of the different machine learning techniques for handwriting to text conversion. It highlights the works of many researchers and provides an analysis of the various machine-learning techniques that are used for the handwriting to text conversion<strong>.</strong></em>
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Newalkar, Akshat, Himanshu Khade, Dhiraj Khandare, and Divy Patel. "CNN-Powered Handwriting to Digital Text Converter." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 5 (2024): 355–66. https://doi.org/10.5281/zenodo.14016761.

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The technology has become a crucial component in the digital transformation of documents for banks, educational institutions, and other sectors. In this paper, we design a handwritten character-to-text converter using CNN, where the input is a handwritten character and it is converted into computerized text. You see, CNNs are pretty darn good at image processing, and basically, what we're doing in the input debugger is detecting individual characters from a wide range of horrible handwritten gibberish. The model is trained on a dataset of handwritten characters, and its hierarchical feature ex
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Ke, Hong Chang, and De Gang Kong. "A New Text-Independent Handwriting Identification Method." Applied Mechanics and Materials 556-562 (May 2014): 4357–60. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4357.

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For the disadvantage of traditional direction index histogram (DIH) handwriting identification method, an improved text-independent handwriting identification algorithm is presented. The handwriting image which is prepared to test needs pre-processing, then the normalized image can be obtained. Based on the features distance two factors can be extracted: the writing influence factor and the character influence factor. Compared the features of the handwriting image which is prepared to test with the features of the sample handwriting image, The better handwriting identification accuracy rate ca
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Shonenkov, A. V., D. K. Karachev, M. Y. Novopoltsev, M. S. Potanin, D. V. Dimitrov, and A. V. Chertok. "Handwritten text generation and strikethrough characters augmentation." Computer Optics 46, no. 3 (2022): 455–64. http://dx.doi.org/10.18287/2412-6179-co-1049.

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We introduce two data augmentation techniques, which, used with a Resnet-BiLSTM-CTC network, significantly reduce Word Error Rate and Character Error Rate beyond best-reported results on handwriting text recognition tasks. We apply a novel augmentation that simulates strikethrough text (HandWritten Blots) and a handwritten text generation method based on printed text (StackMix), which proved to be very effective in handwriting text recognition tasks. StackMix uses weakly-supervised framework to get character boundaries. Because these data augmentation techniques are independent of the network
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Chen, Rui, Bin Fang, and Patrick Shen-Pei Wang. "Chinese Handwriting Identification Method Based on Keyword Extraction." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 11 (2017): 1753004. http://dx.doi.org/10.1142/s0218001417530044.

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Text-independent handwriting identification methods require that features such as texture are extracted from lengthy document image; while text-dependent handwriting identification methods require that the contents of the documents being compared are identical. In order to overcome these confinements, this paper presents a novel Chinese handwriting identification technique. First, Chinese characters are segmented from handwriting document, then keywords are extracted based on matching and voting of local features of character. Then the same-content keywords are used to build training sets, and
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Leedham, C. G., and Y. Qiao. "High speed text input to computer using handwriting." Instructional Science 21, no. 1-3 (1992): 209–21. http://dx.doi.org/10.1007/bf00119666.

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Pramodita, Potta, P. Nancy Celine, and Gugulothu Krishnaveni. "Handwritten Text Recognition: A Deep Learning Based Approach to Digitize Handwritten Text." International Scientific Journal of Engineering and Management 04, no. 07 (2025): 1–9. https://doi.org/10.55041/isjem03136.

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Our project employs deep learning methods to digitize handwritten pages into text. The process has a number of steps, ranging from identifying individual words, and identifying individual characters. Through the use of convolutional neural networks (CNNs) and other sophisticated machine learning models, the project seeks to perform accurate and efficient recognition of handwritten text. The model is trained on the IAM Handwriting Dataset, which contains a diverse collection of handwritten text samples, allowing the system to generalize well across different handwriting styles. Key components o
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Shrawankar, Urmila. "Standardization of Handwritten Words to Improve Readability." International Journal of Technology Diffusion 10, no. 3 (2019): 1–17. http://dx.doi.org/10.4018/ijtd.2019070101.

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Everyone has different handwriting, and this is a difficulty since not everyone can discern the handwriting of different people. The described technique converts unstructured handwriting into a structured type. To resolve the problem, the authors want to separate every letter of the words so as to converting the alphabet into a commonplace type. This is to simplify the written language for everyone. This article presents a piece of text identification from image. OCR is enforced to convert electronic kind of image into machine-editable text.
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Dissertations / Theses on the topic "Handwriting-to-Text"

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Wigington, Curtis Michael. "End-to-End Full-Page Handwriting Recognition." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7099.

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Despite decades of research, offline handwriting recognition (HWR) of historical documents remains a challenging problem, which if solved could greatly improve the searchability of online cultural heritage archives. Historical documents are plagued with noise, degradation, ink bleed-through, overlapping strokes, variation in slope and slant of the writing, and inconsistent layouts. Often the documents in a collection have been written by thousands of authors, all of whom have significantly different writing styles. In order to better capture the variations in writing styles we introduce a nove
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España, Boquera Salvador. "Contributions to the joint segmentation and classification of sequences (My two cents on decoding and handwriting recognition)." Doctoral thesis, Universitat Politècnica de València, 2016. http://hdl.handle.net/10251/62215.

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[EN] This work is focused on problems (like automatic speech recognition (ASR) and handwritten text recognition (HTR)) that: 1) can be represented (at least approximately) in terms of one-dimensional sequences, and 2) solving these problems entails breaking the observed sequence down into segments which are associated to units taken from a finite repertoire. The required segmentation and classification tasks are so intrinsically interrelated ("Sayre's Paradox") that they have to be performed jointly. We have been inspired by what some works call the "successful trilogy", which refers to the
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Williams, Kyle. "Learning to Read Bushman: Automatic Handwriting Recognition for Bushman Languages." Thesis, 2012. http://pubs.cs.uct.ac.za/archive/00000791/.

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The Bleek and Lloyd Collection contains notebooks that document the tradition, language and culture of the Bushman people who lived in South Africa in the late 19th century. Transcriptions of these notebooks would allow for the provision of services such as text-based search and text-to-speech. However, these notebooks are currently only available in the form of digital scans and the manual creation of transcriptions is a costly and time-consuming process. Thus, automatic methods could serve as an alternative approach to creating transcriptions of the text in the notebooks. In order to evalua
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Books on the topic "Handwriting-to-Text"

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From Handwriting to Footprinting: Text and Heritage in the Age of Climate Crisis. Open Book Publishers, 2023.

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From Handwriting to Footprinting: Text and Heritage in the Age of Climate Crisis. Open Book Publishers, 2023.

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Graphology; How to Read Character from Handwriting; Studies in Character Reading, a Text-Book of Graphology for Experts, Students and Laymen. Creative Media Partners, LLC, 2022.

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Hagen, Hugo J. von. Graphology; How to Read Character from Handwriting; Studies in Character Reading, a Text-Book of Graphology for Experts, Students and Laymen. Creative Media Partners, LLC, 2018.

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Hagen, Hugo J. von. Graphology; how to Read Character From Handwriting; Studies in Character Reading, a Text-book of Graphology for Experts, Students and Laymen. Franklin Classics, 2018.

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Hagen, Hugo J. von. Graphology; How to Read Character from Handwriting; Studies in Character Reading, a Text-Book of Graphology for Experts, Students and Laymen. Creative Media Partners, LLC, 2018.

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Noppasorn, Nicharee. Blank Calligraphy Paper Sheets: Italic Calligraphy Handwriting Exercises and Text, Hand Style Lettering from Calligraphy to Typography, Incorporating Modern Flourishes into Your Lettering, Lettered Alphabet Templates. Independently Published, 2021.

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de Bruyn, Theodore. Materials, Format, and Writing. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199687886.003.0003.

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This chapter describes qualities of amulets used throughout the book when examining individual artefacts: the materials on which amulets were written (mainly papyrus and parchment); the format of the written text; the various types of handwriting; lectional signs, spelling, and syntax; acoustic and visual devices used in incantations (vowels, nomina barbara, voces mysticae, charaktêres, word-shapes, figures); and abbreviations or signs used by Christian scribes (nomina sacra, crosses, staurograms, christograms, and other markers). The way in which an amulet is written, the extent to which it d
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Howcroft, Sharalyn D. A Textual and Archival Reexamination of Lucy Mack Smith’s History. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190274375.003.0011.

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Previous scholarly treatment of the history of Lucy Mack Smith has explained the work of Martha and Howard Coray in editing and compiling it. In “A Textual and Archival Reexamination of Lucy Mack Smith’s History,” Sharalyn D. Howcroft applies the archival principle of fonds to the history by reconstructing the original order of the rough manuscript inscribed by Martha Coray, shedding light on the history’s composition methodology and on its extant and non-extant manuscripts. Handwriting in the rough manuscript indicates Martha inscribed roughly half of the history before her husband Howard beg
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Sobecki, Sebastian. Last Words. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198790778.001.0001.

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No medieval text was designed to be read hundreds of years later by an audience unfamiliar with its language, situation, and author. By ascribing to these texts intentional anonymity, we romanticize them and misjudge the social character of their authors. Instead, most medieval poems and manuscripts presuppose familiarity with their authorial or scribal maker. Last Words: The Public Self and the Social Author in Late Medieval England attempts to recover this familiarity and understand the literary motivation behind some of the most important fifteenth-century texts and authors. Last Words capt
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Book chapters on the topic "Handwriting-to-Text"

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Leedham, C. G., and Y. Qiao. "High Speed Text Input to Computer using Handwriting." In Computers and Writing. Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2674-8_16.

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Baillot, Anne. "3. What the Climate Crisis Does to Text." In From Handwriting to Footprinting. Open Book Publishers, 2023. http://dx.doi.org/10.11647/obp.0355.03.

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This chapter proposes a reassessment of the quality criteria for digital text developed in the previous chapter in the light of the climate crisis. It considers the environmental cost of the quantity, quality, circulation and complexity of information that has now been reached in north-western countries. Elaborating on criteria for measuring the environmental impact of text production, dissemination and preservation, this chapter envisions tomorrow's strategies for textual content in a context of greater respect for the limited natural resources that are at our disposal, arguing that the limit
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Wang, Zhihao, Yanwei Yu, Yibo Wang, Haixu Long, and Fazheng Wang. "Robust End-to-End Offline Chinese Handwriting Text Page Spotter with Text Kernel." In Document Analysis and Recognition – ICDAR 2021 Workshops. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86159-9_2.

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Baillot, Anne. "1. Archiving Text." In From Handwriting to Footprinting. Open Book Publishers, 2023. http://dx.doi.org/10.11647/obp.0355.01.

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This chapter examines access to text from the point of view of archives, encompassing both archives as institutions with a dedicated building and archives as document collections. Starting from a historical approach to archiving techniques (preservation conditions, record-keeping, appraisal), it then moves to the specificities of digital archiving and explores similarities and divergences between analog and digital archives. It also proposes a more theoretical approach to archival material as a fleeting trace of things past, based among others on Derrida’s analysis. This chapter demonstrates h
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Sakurada, Takeshi, Yoichi Hagiwara, Hideto Oda, and Masaki Nakagawa. "A Japanese Text Input Interface Using On-Line Writing-Box-Free Handwriting Recognition and Kana-to-Kanji Conversion." In Human Interface and the Management of Information. Methods, Techniques and Tools in Information Design. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73345-4_106.

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Gheno, Vera. "Di lessico e altre quisquilie: autopercezione ed eteropercezione linguistica nel social networking." In Competenza lessicale e apprendimento dell’Italiano L2. Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-403-8.10.

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Digital writing and handwriting are cognitively different activities; thus, it is interesting to verify what happens to the written language when it is conveyed through a keyboard and no longer a pen. On the Net, where the text is stripped of all non-strictly linguistic clues such as tone of voice, facial expression, proxemics, gestures, etc., written words assume a large part of the communicative burden: in other words, we are what we type. Despite this, it often seems that users are not fully aware of how much their linguistic choices affect the construction of their reputation on the net. I
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Baillot, Anne. "2. Publishing, Editing, and Their Digital Transformation." In From Handwriting to Footprinting. Open Book Publishers, 2023. http://dx.doi.org/10.11647/obp.0355.02.

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This chapter focuses on literary texts. It discusses the shifts in text dissemination brought about by publishers in the European context. Publishers became essential professionals in the book market during the 18th and 19th centuries: this chapter examines how they contributed to shaping oeuvres of great authors, illustrated by the two examples of German writers Goethe and Tieck. It then bridges the gap to digitisation processes of modern textual heritage, examining the changes created by the digital medium. It discusses ways of representing text in a digital context, explaining in more detai
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Baillot, Anne. "4. Conclusion." In From Handwriting to Footprinting. Open Book Publishers, 2023. http://dx.doi.org/10.11647/obp.0355.04.

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The conclusion reflects on the limits of technologisation for textual heritage. While creating cultural artefacts is destroying natural resources, this chapter argues that as a society, even as a global one, we have been able to build the material and intellectual conditions to provide access to community-building cultural artefacts, especially text. It encourages today's actors to envision a future where access to text is a common good that even more societies can rely on than it is the case today.
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Baillot, Anne. "0. Introduction." In From Handwriting to Footprinting. Open Book Publishers, 2023. http://dx.doi.org/10.11647/obp.0355.05.

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The introduction delineates the main topic of the book. Starting with archiving, it examines the ways in which text has been handled in north-western cultures since Antiquity. Archiving, editing and publishing are presented as core cultural practices in which text plays a key role and which the following chapters aim to explore. The two main perspectives are a theoretical approach reflecting on their historical institutionalization and an analysis of the impact of the shifts brought on to them at the end of the 20th century with the introduction of digital media and the consequences of the cli
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Kiessling, Benjamin. "CurT: End-to-End Text Line Detection in Historical Documents with Transformers." In Frontiers in Handwriting Recognition. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21648-0_3.

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Conference papers on the topic "Handwriting-to-Text"

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Saaduddin, Syed, Varun Teja, Anish Kumar, Prabhat Rathore, and Kanaiya V. Kanzaria. "From Handwriting to Hypertext: A Seamless Approach to Digital Text Conversion." In 2025 International Conference on Knowledge Engineering and Communication Systems (ICKECS). IEEE, 2025. https://doi.org/10.1109/ickecs65700.2025.11035186.

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Bolognino, John, Sarel Cohen, Eden Bar, et al. "Visible coalitions of neuronal activities in brain-to-text communication via handwriting." In Seventeenth International Conference on Machine Vision (ICMV 2024), edited by Wolfgang Osten. SPIE, 2025. https://doi.org/10.1117/12.3056602.

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Castro, Dayvid, Byron Leite Dantas Bezerra, and Cleber Zanchettin. "An End-to-End Approach for Handwriting Recognition: From Handwritten Text Lines to Complete Pages." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2024. http://dx.doi.org/10.1109/cvprw63382.2024.00031.

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Noubigh, Zouhaira, Anis Mezghani, and Monji Kherallah. "Transfer Learning to improve Arabic handwriting text Recognition." In 2020 21st International Arab Conference on Information Technology (ACIT). IEEE, 2020. http://dx.doi.org/10.1109/acit50332.2020.9300105.

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Chowdhury, Sadia, Farhan Rahman Wasee, Mohammad Shafiqul Islam, and Hasan U. Zaman. "Bengali Handwriting Recognition and Conversion to Editable Text." In 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC). IEEE, 2018. http://dx.doi.org/10.1109/icaecc.2018.8479487.

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Mukherjee, Salankara, and Ishita De Ghosh. "Feature Extraction from Text Images to Study Individuality of Handwriting." In 2020 4th International Conference on Computational Intelligence and Networks (CINE). IEEE, 2020. http://dx.doi.org/10.1109/cine48825.2020.234387.

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Willett, Francis R., Donald T. Avansino, Leigh R. Hochberg, Jaimie M. Henderson, and Krishna V. Shenoy. "Next-generation BCIs: Brain-to-text Communication via Attempted Handwriting." In 2022 10th International Winter Conference on Brain-Computer Interface (BCI). IEEE, 2022. http://dx.doi.org/10.1109/bci53720.2022.9735049.

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Slimane, Fouad, and Volker Margner. "A New Text-Independent GMM Writer Identification System Applied to Arabic Handwriting." In 2014 14th International Conference on Frontiers in Handwriting Recognition (ICFHR). IEEE, 2014. http://dx.doi.org/10.1109/icfhr.2014.124.

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Castro, Dayvid, Cleber Zanchettin, and Byron Bezerra. "An End-to-End Approach for Handwriting Recognition: From Handwritten Text Lines to Complete Manuscripts." In LatinX in AI at Computer Vision and Pattern Recognition Conference 2024. Journal of LatinX in AI Research, 2024. http://dx.doi.org/10.52591/lxai202406171.

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Handwritten Document Recognition (HDR) has emerged as a challenging task integrating text and layout information recognition to tackle manuscripts end-to-end. Despite advancements, the computational efficiency of processing entire documents remains a critical challenge, limiting the practical applicability of these models. This paper presents the Document Attention Network for Computationally Effi cient Recognition (DANCER). The model differs from existing approaches with its unique encoder-decoder structure, where the encoder reduces spatial redundancy and enhances spatial attention, and the
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Sebastian, Christian Josefh M., Mark William J. Rogala, and Charmaine C. Paglinawan. "Neurological Disorder Rooted-Impaired Handwriting to Text Using Neural Network Image Processing for Special Education Applications." In 2024 16th International Conference on Computer and Automation Engineering (ICCAE). IEEE, 2024. http://dx.doi.org/10.1109/iccae59995.2024.10569849.

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