Annotating historical archives of images
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
Handwritten word-spotting using hidden Markov models and universal vocabularies
Pattern Recognition
A comprehensive evaluation methodology for noisy historical document recognition techniques
Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data
Unsupervised writer adaptation of whole-word HMMs with application to word-spotting
Pattern Recognition Letters
A line-based representation for matching words in historical manuscripts
Pattern Recognition Letters
Image retrieval systems based on compact shape descriptor and relevance feedback information
Journal of Visual Communication and Image Representation
A keyword spotting approach using blurred shape model-based descriptors
Proceedings of the 2011 Workshop on Historical Document Imaging and Processing
Lexicon-free handwritten word spotting using character HMMs
Pattern Recognition Letters
Dynamic Time Warping for Chinese calligraphic character matching and recognizing
Pattern Recognition Letters
Learning-based word spotting system for Arabic handwritten documents
Pattern Recognition
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Effective indexing is crucial for providing convenient access to scanned versions of large collections of historically valuable handwritten manuscripts. Since traditional handwriting recognizers based on optical character recognition (OCR) do not perform well on historical documents, recently a holistic word recognition approach has gained in popularity as an attractive and more straightforward solution (Lavrenko et al. in proc. document Image Analysis for Libraries (DIAL’04), pp. 278–287, 2004). Such techniques attempt to recognize words based on scalar and profile-based features extracted from whole word images. In this paper, we propose a new approach to holistic word recognition for historical handwritten manuscripts based on matching word contours instead of whole images or word profiles. The new method consists of robust extraction of closed word contours and the application of an elastic contour matching technique proposed originally for general shapes (Adamek and O’Connor in IEEE Trans Circuits Syst Video Technol 5:2004). We demonstrate that multiscale contour-based descriptors can effectively capture intrinsic word features avoiding any segmentation of words into smaller subunits. Our experiments show a recognition accuracy of 83%, which considerably exceeds the performance of other systems reported in the literature.