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Handwritten-Word Spotting Using Biologically Inspired Features
November 2008 (vol. 30 no. 11)
pp. 1945-1957
ASCII Text
x 
 
Tijn van der Zant, Lambert Schomaker, Koen Haak, "Handwritten-Word Spotting Using Biologically Inspired Features," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 11, pp. 1945-1957, November, 2008.
 
 
BibTex
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@article{ 10.1109/TPAMI.2008.144,
author = {Tijn van der Zant and Lambert Schomaker and Koen Haak},
title = {Handwritten-Word Spotting Using Biologically Inspired Features},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {30},
number = {11},
issn = {0162-8828},
year = {2008},
pages = {1945-1957},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.144},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Handwritten-Word Spotting Using Biologically Inspired Features
IS - 11
SN - 0162-8828
SP1945
EP1957
EPD - 1945-1957
A1 - Tijn van der Zant,
A1 - Lambert Schomaker,
A1 - Koen Haak,
PY - 2008
KW - Handwriting analysis
KW - Interactive systems
KW - Image/video retrieval
KW - Computer vision
KW - Computational neuroscience
KW - Digital Libraries
KW - Information Storage and Retrieval
KW - Information Technology and Systems
KW - Invariants
KW - Feature Measurement
KW - Image Processing and Computer Vision
KW - Computing Methodologies
VL - 30
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Tijn van der Zant, University of Groningen, Groningen
Lambert Schomaker, University of Groningen, Groningen
Koen Haak, University of Groningen, Groningen
For quick access to new handwritten collections, current handwriting recognition methods are too cumbersome. They cannot deal with the lack of labeled data and would require extensive laboratory training for each individual script, style, language and collection. We propose a biologically inspired whole-word recognition method which is used to incrementally elicit word labels in a live, web-based annotation system, named Monk. Since human labor should be minimized given the massive amount of image data, it becomes important to rely on robust perceptual mechanisms in the machine. Recent computational models of the neuro-physiology of vision are applied to isolated word classification. A primate cortex-like mechanism allows to classify text-images that have a low frequency of occurrence. Typically these images are the most difficult to retrieve and often contain named entities and are regarded as the most important to people. Usually standard pattern-recognition technology cannot deal with these text-images if there are not enough labeled instances. The results of this retrieval system are compared to normalized word-image matching and appear to be very promising.

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Index Terms:
Handwriting analysis, Interactive systems, Image/video retrieval, Computer vision, Computational neuroscience, Digital Libraries, Information Storage and Retrieval,Information Technology and Systems, Invariants, Feature Measurement, Image Processing and Computer Vision, Computing Methodologies
Citation:
Tijn van der Zant, Lambert Schomaker, Koen Haak, "Handwritten-Word Spotting Using Biologically Inspired Features," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 11, pp. 1945-1957, Nov. 2008, doi:10.1109/TPAMI.2008.144
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