A novel word segmentation method based on object detection and deep learning
2015 (English)In: Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part I / [ed] Bebis, G; Boyle, R; Parvin, B; Koracin, D; Pavlidis, I; Feris, R; McGraw, T; Elendt, M; Kopper, R; Ragan, E; Ye, Z; Weber, G, Springer, 2015, 231-240 p.Conference paper (Refereed)
The segmentation of individual words is a crucial step in several data mining methods for historical handwritten documents. Examples of applications include visual searching for query words (word spotting) and character-by-character text recognition. In this paper, we present a novel method for word segmentation that is adapted from recent advances in computer vision, deep learning and generic object detection. Our method has unique capabilities and it has found practical use in our current research project. It can easily be trained for different kinds of historical documents, uses full gray scale information, does not require binarization as pre-processing or prior segmentation of individual text lines. We evaluate its performance using established error metrics, previously used in competitions for word segmentation, and demonstrate its usefulness for a 15th century handwritten document.
Place, publisher, year, edition, pages
Springer, 2015. 231-240 p.
, Lecture Notes in Computer Science, ISSN 0302-9743 ; 9474
Computer Vision and Robotics (Autonomous Systems)
Research subject Computerized Image Processing
IdentifiersURN: urn:nbn:se:uu:diva-272181DOI: 10.1007/978-3-319-27857-5_21ISI: 000376400300021ISBN: 9783319278568ISBN: 9783319278575OAI: oai:DiVA.org:uu-272181DiVA: diva2:893350
ISVC 2015, December 14–16, Las Vegas, NV
FunderSwedish Research Council, 2012-5743