Implementing word retrieval in handwritten documents using a small dataset
Implementing word retrieval in handwritten documents using a small dataset
A novel approach to the problem of keyword retrieval in cursive handwritten documents is introduced in this work. Two issues are addressed: small dataset size and uneven sample distribution across the character set. The proposed strategies utilise graphemes (fragments of a handwritten word) to implement a recognition model which is subsequently used to form the feature model for the query word.
training, character recognition, image segmentation, testing, handwriting recognition, hidden Markov models, training data
728-733
Liang, Yiqing
e6019ef2-d232-4bce-a224-fa21984a61d8
Guest, Richard
93533dbd-b101-491b-83cc-39ccfdc18165
Fairhurst, Michael
6a82d154-93fe-4657-bcee-934d5c888192
18 September 2012
Liang, Yiqing
e6019ef2-d232-4bce-a224-fa21984a61d8
Guest, Richard
93533dbd-b101-491b-83cc-39ccfdc18165
Fairhurst, Michael
6a82d154-93fe-4657-bcee-934d5c888192
Liang, Yiqing, Guest, Richard and Fairhurst, Michael
(2012)
Implementing word retrieval in handwritten documents using a small dataset.
In 2012 International Conference on Frontiers in Handwriting Recognition.
IEEE; Wiley.
.
(doi:10.1109/ICFHR.2012.220).
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Conference or Workshop Item
(Paper)
Abstract
A novel approach to the problem of keyword retrieval in cursive handwritten documents is introduced in this work. Two issues are addressed: small dataset size and uneven sample distribution across the character set. The proposed strategies utilise graphemes (fragments of a handwritten word) to implement a recognition model which is subsequently used to form the feature model for the query word.
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Published date: 18 September 2012
Keywords:
training, character recognition, image segmentation, testing, handwriting recognition, hidden Markov models, training data
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Local EPrints ID: 489819
URI: http://eprints.soton.ac.uk/id/eprint/489819
PURE UUID: ba40403e-dcc0-4fcf-8d9d-ba35bb52d961
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Date deposited: 02 May 2024 16:52
Last modified: 03 May 2024 02:07
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Contributors
Author:
Yiqing Liang
Author:
Richard Guest
Author:
Michael Fairhurst
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