Paper Info
Title
Finding Images and Line-Drawings in Document-Scanning Systems
Abstract
The system presented in this paper finds images and line-drawings in scanned pages; it is a crucial processing step in the creation of a large-scale system to detect and index images found in books and historic documents. Within the scanned pages that contain both text and images, the images are found through the use of SIFT-based local-features applied to the complete scanned-page. This is followed by a novel learning system to categorize the found SIFT features into either text or image. The discrimination is based on using multiple classifiers trained via AdaBoost. Through the use of this system, we improve image detection by finding more line-drawings, graphics, and photographs, as well as by reducing the number of spurious detections due to misclassified text, discolorations, and scanning artifacts.
Year
DOI
Venue
2009
10.1109/ICDAR.2009.106
ICDAR-1
Keywords
Field
DocType
Ada,document image processing,indexing,large-scale systems,learning (artificial intelligence),object detection,pattern classification,AdaBoost,SIFT-based local-features,classifiers,document-scanning systems,image detection,image finding,image indexing,large-scale system,line-drawings,novel learning system,document scanning,historic books,historic manuscripts,local descriptors
Graphics,Scale-invariant feature transform,Object detection,Histogram,Computer vision,AdaBoost,Pattern recognition,Computer science,Search engine indexing,Feature extraction,Artificial intelligence,Line drawings
Conference
Citations 
PageRank 
References 
2
0.48
8
Authors
2
Name
Order
Citations
PageRank
Shumeet Baluja14053728.83
Michele Covell270678.42