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Font Adaptive Word Indexing of Modern Printed Documents
August 2006 (vol. 28 no. 8)
pp. 1187-1199
ASCII Text
x 
 
Simone Marinai, Emanuele Marino, Giovanni Soda, "Font Adaptive Word Indexing of Modern Printed Documents," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 8, pp. 1187-1199, August, 2006.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2006.162,
author = {Simone Marinai and Emanuele Marino and Giovanni Soda},
title = {Font Adaptive Word Indexing of Modern Printed Documents},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {28},
number = {8},
issn = {0162-8828},
year = {2006},
pages = {1187-1199},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2006.162},
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 - Font Adaptive Word Indexing of Modern Printed Documents
IS - 8
SN - 0162-8828
SP1187
EP1199
EPD - 1187-1199
A1 - Simone Marinai,
A1 - Emanuele Marino,
A1 - Giovanni Soda,
PY - 2006
KW - Clustering
KW - digital libraries
KW - document image retrieval
KW - heuristic oversegmentation
KW - holistic word representation
KW - modern documents
KW - self organizing map.
VL - 28
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
We propose an approach for the word-level indexing of modern printed documents which are difficult to recognize using current OCR engines. By means of word-level indexing, it is possible to retrieve the position of words in a document, enabling queries involving proximity of terms. Web search engines implement this kind of indexing, allowing users to retrieve Web pages on the basis of their textual content. Nowadays, digital libraries hold collections of digitized documents that can be retrieved either by browsing the document images or relying on appropriate metadata assembled by domain experts. Word indexing tools would therefore increase the access to these collections. The proposed system is designed to index homogeneous document collections by automatically adapting to different languages and font styles without relying on OCR engines for character recognition. The approach is based on three main ideas: the use of Self Organizing Maps (SOM) to perform unsupervised character clustering, the definition of one suitable vector-based word representation whose size depends on the word aspect-ratio, and the run-time alignment of the query word with indexed words to deal with broken and touching characters. The most appropriate applications are for processing modern printed documents (17th to 19th centuries) where current OCR engines are less accurate. Our experimental analysis addresses six data sets containing documents ranging from books of the 17th century to contemporary journals.

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Index Terms:
Clustering, digital libraries, document image retrieval, heuristic oversegmentation, holistic word representation, modern documents, self organizing map.
Citation:
Simone Marinai, Emanuele Marino, Giovanni Soda, "Font Adaptive Word Indexing of Modern Printed Documents," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 8, pp. 1187-1199, Aug. 2006, doi:10.1109/TPAMI.2006.162
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