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Raising the Titanic: Prospects for Reviving the Century Dictionary ...
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Exploiting Script Similarities to Compensate for the Large Amount of Data in Training Tesseract LSTM: Towards Kurdish OCR
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In: Applied Sciences ; Volume 11 ; Issue 20 (2021)
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Reconocimiento automático de un censo histórico impreso sin recursos lingüísticos
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Anitei, Dan. - : Universitat Politècnica de València, 2021
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Quality Measurement for Optical Character Recognition without ground truth data ...
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Quality Measurement for Optical Character Recognition without ground truth data ...
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Improving the recognition of Dutch Gothic machine print, at four levels in the processing pipeline, in four days ...
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Improving the recognition of Dutch Gothic machine print, at four levels in the processing pipeline, in four days ...
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NAT: Noise-Aware Training for Robust Neural Sequence Labeling
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In: Fraunhofer IAIS (2020)
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OPTICAL CHARACTER RECOGNITION APPLIED TO ANDROID-BASED BILINGUAL TRANSLATOR APPLICATION (ENGLISH AND INDONESIAN) TO SIGN LANGUAGE ...
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OPTICAL CHARACTER RECOGNITION APPLIED TO ANDROID-BASED BILINGUAL TRANSLATOR APPLICATION (ENGLISH AND INDONESIAN) TO SIGN LANGUAGE ...
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Bilingual text detection in natural scene images using invariant moments
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Wenn Algorithmen Zeitschriften lesen - vom Mehrwert automatisierter Textanreicherung ...
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Generating a training corpus for OCR post-correction using encoder-decoder model
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In: Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers) ; International Joint Conference on Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-01831147 ; International Joint Conference on Natural Language Processing, Nov 2017, Taipei, Taiwan ; https://www.aclweb.org/anthology/I17-1101 (2017)
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Corpus linguistics for History ... : the methodology of investigating place-name discourses in digitised nineteenth-century newspapers ...
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Radical Recognition in Off-Line Handwritten Chinese Characters Using Non-Negative Matrix Factorization
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In: Senior Projects Spring 2016 (2016)
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Abstract:
In the past decade, handwritten Chinese character recognition has received renewed interest with the emergence of touch screen devices. Other popular applications include on-line Chinese character dictionary look-up and visual translation in mobile phone applications. Due to the complex structure of Chinese characters, this classification task is not exactly an easy one, as it involves knowledge from mathematics, computer science, and linguistics. Given a large image database of handwritten character data, the goal of my senior project is to use Non-Negative Matrix Factorization (NMF), a recent method for finding a suitable representation (parts-based representation) of image data, to detect specific sub-components in Chinese characters. NMF has only been applied to typed (printed) Chinese characters in different fonts. This project focuses specifically on how well NMF works on handwritten characters. In addition, research in Chinese character classification has mainly been done using holistic approaches - treating each character as an inseparable unit. By using NMF, this project takes a different approach by focusing on a more specific problem in Chinese character classification: radical (sub-component) detection. Finally, a possible application of radical detection will be proposed. This interactive application can potentially help Chinese language learners better recognize characters by radicals.
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Keyword:
Applied Linguistics; Applied Mathematics; Applied Statistics; Artificial Intelligence and Robotics; Chinese Character Recognition; Computational Linguistics; Computer Sciences; Graphics and Human Computer Interfaces; Linguistics; Machine Learning; Non-Negative Matrix Factorization; Optical Character Recognition
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URL: https://digitalcommons.bard.edu/cgi/viewcontent.cgi?article=1176&context=senproj_s2016 https://digitalcommons.bard.edu/senproj_s2016/367
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Drifting through Basic Subprocesses of Reading: A Hierarchical Diffusion Model Analysis of Age Effects on Visual Word Recognition
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In: ISSN: 1664-1078 ; Frontiers in Psychology ; https://hal-amu.archives-ouvertes.fr/hal-01522738 ; Frontiers in Psychology, Frontiers, 2016, 7, pp.1863 - 1863. ⟨10.3389/fpsyg.2016.01863⟩ (2016)
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Using SMT for OCR error correction of historical texts
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In: Afli, Haithem orcid:0000-0002-7449-4707 , Qui, Zhengwei, Way, Andy orcid:0000-0001-5736-5930 and Sheridan, Páraic (2016) Using SMT for OCR error correction of historical texts. In: Tenth International Conference on Language Resources and Evaluation (LREC 2016), 23-28 May 2016, Portorož, Slovenia. ISBN 978-2-9517408-9-1 (2016)
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