Abstract book (pdf) - ICPR 2010

09:20-09:40, Paper ThAT7.2
HMM-Based Word Spotting in Handwritten Documents using Subword Models
Fischer, Andreas, Univ. of Bern
Keller, Andreas, Univ. of Bern
Frinken, Volkmar, Univ. of Bern
Bunke, Horst, Univ. of Bern
Handwritten word spotting aims at making document images amenable to browsing and searching by keyword retrieval.
In this paper, we present a word spotting system based on Hidden Markov Models (HMM) that uses trained subword models
to spot keywords. With the proposed method, arbitrary keywords can be spotted that do not need to be present in the
training set. Also, no text line segmentation is required. On the modern IAM off-line database and the historical George
Washington database we show that the proposed system outperforms a standard template matching approach based on dynamic
time warping (DTW).
09:40-10:00, Paper ThAT7.3
A Content Spotting System for Line Drawing Graphic Document Images
Luqman, Muhammad Muzzamil, Univ. Françoise Rabelaise Tours France; CVC Barcelona
Brouard, Thierry, Univ. Françoise Rabelaise Tours France
Ramel, Jean-Yves, Univ. François Rabelais de Tours
Llados, Josep, Computer Vision Center
We present a content spotting system for line drawing graphic document images. The proposed system is sufficiently domain
independent and takes the keyword based information retrieval for graphic documents, one step forward, to Query
By Example (QBE) and focused retrieval. During offline learning mode: we vectorize the documents in the repository,
represent them by attributed relational graphs, extract regions of interest (ROIs) from them, convert each ROI to a fuzzy
structural signature, cluster similar signatures to form ROI classes and build an index for the repository. During online
querying mode: a Bayesian network classifier recognizes the ROIs in the query image and the corresponding documents
are fetched by looking up in the repository index. Experimental results are presented for synthetic images of architectural
and electronic documents.
10:00-10:20, Paper ThAT7.4
Toward Massive Scalability in Image Matching
Moraleda, Jorge, Ricoh Innovations Inc.
Hull, Jonathan, Ricoh
A method for image matching from partial blurry images is presented that leverages existing text retrieval algorithms to
provide a solution that scales to hundreds of thousands of images. As an initial application, we present a document image
matching system in which the user supplies a query image of a small patch of a paper document taken with a cell phone
camera, and the system returns a label identifying the original electronic document if found in a previously indexed collection.
Experimental results show that a retrieval rate of over 70% is achieved on a collection of nearly 500,000 document
pages.
10:20-10:40, Paper ThAT7.5
Learning Image Anchor Templates for Document Classification and Data Extraction
Sarkar, Prateek, Palo Alto Res. Center
Image anchor templates are used in document image analysis for document classification, data localization, and other
tasks. Current tools allow human operators to mark out small sub-images from documents to act as anchor templates.
However, this requires time, and expertise because operators have to make informed decisions based on behavior of the
template matching algorithms, and the expected degradations patterns in documents. We propose learning templates for a
task automatically and quickly from a few training examples. Document classification or data localization can be done
more robustly by combining evidence from many more discriminating templates (e.g., hundreds) than would be practicable
for operators to specify.
- 249 -