Anders Hast
ABSTRACT
Transcription of large-scale historical handwritten document images is a tedious task. Machine learning techniques, such as deep learning, are popularly used for quick transcription, but often require a substantial amount of pre-transcribed word examples for training. Instead of line-by-line word transcription, this paper proposes a simple training-free gamification strategy where all occurrences of each arbitrarily selected word is transcribed once, using an intelligent user interface implemented in this work. The proposed approach offers a fast and user-friendly semi-automatic transcription that allows multiple users to work on the same document collection simultaneously.
- Vicent Alabau and Luis Leiva. 2012. Transcribing handwritten text images with a word soup game. In CHI'12 Extended Abstracts on Human Factors in Computing Systems. ACM, 2273--2278. Google Scholar
Digital Library
- Sebastian Deterding, Dan Dixon, Rilla Khaled, and Lennart Nacke. 2011. From game design elements to gamefulness: defining gamification. In Proceedings of the 15th international academic MindTrek conference: Envisioning future media environments. ACM, 9--15. Google ScholarDigital Library
- Anders Hast, Per Cullhed, and Ekta Vats. 2017. TexT - Text Extractor Tool for Handwritten Document Transcription and Annotation. In 14:th Italian Research Conference on Digital Libraries, IRCDL. 1--12.Google Scholar
- Anders Hast and Alicia Fornés. 2016. A Segmentation-free Handwritten Word Spotting Approach by Relaxed Feature Matching. In Document Analysis Systems, 2016 12th IAPR Workshop on. IEEE, 150--155.Google ScholarCross Ref
- Oriol Ramos Terrades, A. H. Toselli, N. Serrano, V. Romero, E. Vidal, and A. Juan. 2010. Interactive layout analysis and transcription systems for historic handwritten documents. In 10th ACM Symposium on Document Engineering. 219--222. Google ScholarDigital Library
Index Terms
- An Intelligent User Interface for Efficient Semi-automatic Transcription of Historical Handwritten Documents
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