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Gemma Sanchez, Alicia Fornes, Joan Mas, & Josep Llados. (2007). Computer Vision Tools for Visually Impaired Children Learning.
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Gemma Sanchez, Alicia Fornes, Joan Mas, & Josep Llados. (2007). Computer Vision Tools for Visually Impaired Children Learning.
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Mohamed Ali Souibgui, Asma Bensalah, Jialuo Chen, Alicia Fornes, & Michelle Waldispühl. (2023). A User Perspective on HTR methods for the Automatic Transcription of Rare Scripts: The Case of Codex Runicus Just Accepted. JOCCH - ACM Journal on Computing and Cultural Heritage, 15(4), 1–18.
Abstract: Recent breakthroughs in Artificial Intelligence, Deep Learning and Document Image Analysis and Recognition have significantly eased the creation of digital libraries and the transcription of historical documents. However, for documents in rare scripts with few labelled training data available, current Handwritten Text Recognition (HTR) systems are too constraint. Moreover, research on HTR often focuses on technical aspects only, and rarely puts emphasis on implementing software tools for scholars in Humanities. In this article, we describe, compare and analyse different transcription methods for rare scripts. We evaluate their performance in a real use case of a medieval manuscript written in the runic script (Codex Runicus) and discuss advantages and disadvantages of each method from the user perspective. From this exhaustive analysis and comparison with a fully manual transcription, we raise conclusions and provide recommendations to scholars interested in using automatic transcription tools.
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S.Chanda, Umapada Pal, & Oriol Ramos Terrades. (2009). Word-Wise Thai and Roman Script Identification. TALIP - ACM Transactions on Asian Language Information Processing, 1–21.
Abstract: In some Thai documents, a single text line of a printed document page may contain words of both Thai and Roman scripts. For the Optical Character Recognition (OCR) of such a document page it is better to identify, at first, Thai and Roman script portions and then to use individual OCR systems of the respective scripts on these identified portions. In this article, an SVM-based method is proposed for identification of word-wise printed Roman and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of a character group combining different character features obtained from structural shape, profile behavior, component overlapping information, topological properties, and water reservoir concept, etc. Based on the experiment on 10,000 data (words) we obtained 99.62% script identification accuracy from the proposed scheme.
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Antonio Lopez, Ernest Valveny, & Juan J. Villanueva. (2005). Real-time quality control of surgical material packaging by artificial vision. Assembly Automation, 25(3).
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