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Publications (2 of 2) Show all publications
Vats, E., Hast, A. & Mårtensson, L. (2018). Extracting script features from a large corpus of handwritten documents. In: Digital Humanities in the Nordic Countries: Book of Abstracts. Paper presented at DHN 2018, March 7–9, Helsinki, Finland.
Open this publication in new window or tab >>Extracting script features from a large corpus of handwritten documents
2018 (English)In: Digital Humanities in the Nordic Countries: Book of Abstracts, 2018Conference paper, Oral presentation with published abstract (Refereed)
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-356773 (URN)
Conference
DHN 2018, March 7–9, Helsinki, Finland
Available from: 2018-08-06 Created: 2018-08-06 Last updated: 2018-11-10Bibliographically approved
Hast, A. & Vats, E. (2018). Radial line Fourier descriptor for historical handwritten text representation. In: Proc. 26th International Conference on Computer Graphics: Visualization and Computer Vision. Paper presented at WSCG 2018, May 28 – June 1, Pilsen, Czech Republic.
Open this publication in new window or tab >>Radial line Fourier descriptor for historical handwritten text representation
2018 (English)In: Proc. 26th International Conference on Computer Graphics: Visualization and Computer Vision, 2018Conference paper, Published paper (Other academic)
Abstract [en]

Automatic recognition of historical handwritten manuscripts is a daunting task due to paper degradation over time. Recognition-free retrieval or word spotting is popularly used for information retrieval and digitization of the historical handwritten documents. However, the performance of word spotting algorithms depends heavily on feature detection and representation methods. Although there exist popular feature descriptors such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF), the invariant properties of these descriptors amplify the noise in the degraded document images, rendering them more sensitive to noise and complex characteristics of historical manuscripts. Therefore, an efficient and relaxed feature descriptor is required as handwritten words across different documents are indeed similar, but not identical. This paper introduces a Radial Line Fourier (RLF) descriptor for handwritten word representation, with a short feature vector of 32 dimensions. A segmentation-free and training-free handwritten word spotting method is studied herein that relies on the proposed RLF descriptor, takes into account different keypoint representations and uses a simple preconditioner-based feature matching algorithm. The effectiveness of the RLF descriptor for segmentation-free handwritten word spotting is empirically evaluated on well-known historical handwritten datasets using standard evaluation measures.

National Category
Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-351943 (URN)
Conference
WSCG 2018, May 28 – June 1, Pilsen, Czech Republic
Available from: 2018-05-31 Created: 2018-05-31 Last updated: 2018-09-20Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4480-3158

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