RR_03_02

Indexing and Retrieval of Document Images
Using Term Positions and Physical Structures
Koichi Kise, Keinosuke Matsumoto
Dept. of Computer and Systems Sciences, Osaka Prefecture University
1-1 Gakuencho, Sakai, Osaka 599-853 1, Japan
kise@cs.osakafu-u.ac.jp
Abstract
This paper presents some methods of indexing and retrieval
of document images based on their physical (layout)
structures and term positions in pages. Documents are divided
into blocks that are physically de ned for the purpose
of indexing with terms. The simple vector space model
(VSM) and the latent semantic indexing (LSI) are employed
as retrieval models. Experimental results on the retrieval of
129 documents show that LSI with blocks consisting of overlapping
pages outperforms an ordinary method of retrieval
based on the VSM.
1. Introduction
Document image databases (OrBs) are the databases that
provide ef cient storage of and access to document images
[I]. As it becomes more popular to equip copiers and
printers with devices for the storage of document images,
OrBs gain importance in our society.
An open issue of OrBs is how to achieve content-based
retrieval based on queries given by users. In the case that
queries are given as image features such as layout of documents
[2], indexing by layout analysis should be applied. [f
queries are given as keywords, it is required to apply OCR
for indexing. [n addition to the issue of OCR errors [3], we
have another issue of how to index document images based
on recognized characters and words.
A simple way is to employ the Bag of Words model,
which is common in the eld of information retrieval. [n
this model, documents are regarded as collections of words.
[n the context of OrBs, therefore, the rest of information
obtained through the process of OCR, e.g., positions of
characters / words, and physical (layout) structures of documents,
is discarded.
This paper presents methods of util izing some of the discarded
information in addition to words (terms) themselves
19
o··· DO
document (D) page (P)
II ··· D···
column (C) half of a column (H)
Figure 1. Units of indexing.
so as to improve the accuracy of retrieval. To be precise,
documents are indexed based on the information of physical
structures such as pages and columns, as well as positions
of terms in pages. As an example of such methods, we
have already proposed the method called density distributions
of terms [4, 5], which is an application of passage
retrieval in the [R eld to document images. The methods
proposed in this paper can be viewed as simpli ed versions
of this method for the reduction of computational costs.
2. Indexing
Our methods of indexing are de ned by units and blocks
of indexing. As units of indexing, we consider page (P),
column (C) and half of a column (H) in addition to document
(D) as shown in Fig. l.
Columns are obtained in a brute-force manner in order to
avoid complicated problems in layout analysis: each page
region is cut into two pieces at a xed position. As a result,
regions laid out in a si ngle column format as well as
some wider gures and tables are split into different pieces.
Halves of columns are likewise de ned by splitting at the
physical vertical center of columns.