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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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Schomaker, Lambert; Ameryan, Mahya; Cuper, Mirjam; Dercksen, Koen; Guo, Jerry; Koert, Rutger van; Mendrik, Adriënne; Todorov, Konstantin; Wang, Xue. - : Zenodo, 2020
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Abstract:
Libraries and archives are struggling with optical character recognition (OCR) of old machine-print fonts such as Gothic or 'fraktur'. This font was used in many important historical printed collections such as administrative texts and the then (17th century) newly invented 'newspapers' with interesting and detailed reports on important developments and events. When applying current state of the art OCR tools or sending the scanned images to large well-known companies that provide OCR services, the returned results are still quite disappointing. Problems are observed at all levels in the processing pipeline: binarisation suffering from ink bleed-through, layout analysis suffering from deviating page designs, marginalia and graphics, character recognition suffering from lack of pertinent font examples and font variation (Roman/Gothic) in a document and, finally, linguistic post processing suffering from an utter lack of encoded digital text corpora of suitable size. Actually, the OCR process is often intended ...
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Keyword:
blackletter; historical printed collections; ICT with industry; optical character recognition
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URL: https://dx.doi.org/10.5281/zenodo.4003740 https://zenodo.org/record/4003740
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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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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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