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Start Over Topic optical character recognition
7 results

1. Mapping spatial named entities from noisy OCR output: Epimetheus from OCR to map

Author

Koudoro-Parfait, Caroline, Alrahabi, Motasem, Dupont, Yoann, Lejeune, Gaël, Roe, Glenn, Scholger, Walter, Vogeler, Georg, Tasovac, Toma, Baillot, Anne, Raunig, Elisabeth, Scholger, Martina, Steiner, Elisabeth, Centre for Information Modelling, and Helling, Patrick

Subjects
Paper, spatial & spatio-temporal analysis, Statistics, Geography and geo-humanities, optical character recognition and handwriting recognition, Named Entity Recognition, Short Presentation, Literary studies, Cluster, Map, FOS: Mathematics, modeling and visualization, natural language processing, artificial intelligence and machine learning, Optical character recognition
Abstract

This contribution presents the difficulties encountered and methods to overcome them when using ready-to-use tools for the elaboration of a processing chain going from OCR to NER and then to the cartographic representation of spaces mentioned in literary texts.

Published
2023
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3. Reducing Errors from the Electronic Transcription of Data Collected on Paper Forms: A Research Data Case Study

Author

Monika M. Wahi, David V. Parks, Robert Skeate, and Steven B. Goldin

Subjects
Paper, Medical Records Systems, Computerized, Computer science, Health Informatics, Case Report, Data entry, computer.software_genre, Medical Records, User-Computer Interface, Software, Reliability study, Surveys and Questionnaires, Feature (machine learning), Humans, Software system, Research data, Electronic Data Processing, Medical Errors, business.industry, Optical character recognition, Artificial intelligence, Data mining, Forms and Records Control, Transcription (software), business, computer, Natural language processing
Abstract

We conducted a reliability study comparing single data entry (SE) into a Microsoft Excel spreadsheet to entry using the existing forms (EF) feature of the Teleforms software system, in which optical character recognition is used to capture data off of paper forms designed in non-Teleforms software programs. We compared the transcription of data from multiple paper forms from over 100 research participants representing almost 20,000 data entry fields. Error rates for SE were significantly lower than those for EF, so we chose SE for data entry in our study. Data transcription strategies from paper to electronic format should be chosen based on evidence from formal evaluations, and their design should be contemplated during the paper forms development stage.

Published
2008