DocumentCode :
3485410
Title :
IBM_UB_1: A Dual Mode Unconstrained English Handwriting Dataset
Author :
Shivram, Arti ; Ramaiah, Chetan ; Setlur, Srirangaraj ; Govindaraju, Vengatesan
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. at Buffalo, Buffalo, NY, USA
fYear :
2013
fDate :
25-28 Aug. 2013
Firstpage :
13
Lastpage :
17
Abstract :
In this paper we present a new dual mode, twin-folio structured English handwriting dataset IBM_UB_1. IBM_UB_1 is our first major release from a large multilingual handwriting corpus. Containing over 6000 pages of handwritten matter, this dataset can not only be used for unconstrained handwriting recognition, more importantly, the dataset´s unique twin-folio structure presents a natural fit for research on writer identification, keyword spotting, indexing and various forms of handwritten document search and retrieval. We first describe two central characteristics of the dataset - the twin-folio structure and dual modality (online/offline) - and their relevance to current research problems. Secondly, we describe the dataset, its collection and construction, and provide key descriptive statistics. Finally, we evaluate the dataset on two different research domains - handwriting recognition and writer identification - and present related experimental results.
Keywords :
handwriting recognition; image retrieval; IBM_UB_1 dataset; dataset collection; dataset construction; descriptive statistics; dual mode unconstrained English handwriting dataset; handwriting recognition; handwritten document retrieval; handwritten document search; indexing; keyword spotting; multilingual handwriting corpus; twin-folio structured English handwriting dataset; unconstrained handwriting recognition; writer identification; Data collection; Databases; Educational institutions; Handwriting recognition; Hidden Markov models; Text analysis; Writing; Dataset; Dual mode; English online handwriting dataset; Handwriting recognition; Offline-online; Online handwriting dataset; Unconstrained handwriting dataset; Writer Identification; offline/online; twin-folio;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Document Analysis and Recognition (ICDAR), 2013 12th International Conference on
Conference_Location :
Washington, DC
ISSN :
1520-5363
Type :
conf
DOI :
10.1109/ICDAR.2013.12
Filename :
6628577
Link To Document :
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