Paper Info
Title
Shape Codebook Based Handwritten And Machine Printed Text Zone Extraction
Abstract
In this paper, we present a novel method for extracting handwritten and printed text zones from noisy document images with mixed content. We use Triple-Adjacent-Segment (TAS) based features which encode local shape characteristics of text in a consistent manner. We first construct two codebooks of the shape features extracted from a set of handwritten and printed text documents respectively. We then compute the normalized histogram of codewords for each segmented zone and use it to train a Support Vector Machine (SVM) classifier. The codebook based approach is robust to the background noise present in the image and TAS features are invariant to translation, scale and rotation of text. In experiments, we show that a pixel-weighted zone classification accuracy of 98% can be achieved for noisy Arabic documents. Further, we demonstrate the effectiveness of our method for document page classification and show that a high precision can be achieved for the detection of machine printed documents. The proposed method is robust to the size of zones, which may contain text content at line or paragraph level.
Year
DOI
Venue
2011
10.1117/12.876725
DOCUMENT RECOGNITION AND RETRIEVAL XVIII
Keywords
Field
DocType
zone classification, zone segmentation, page classification, noisy documents, handwriting, Arabic
Computer vision,Histogram,Background noise,Normalization (statistics),Pattern recognition,Computer science,Support vector machine,Feature extraction,Pixel,Artificial intelligence,Classifier (linguistics),Codebook
Conference
Volume
ISSN
Citations 
7874
0277-786X
12
PageRank 
References 
Authors
0.84
20
6
Name
Order
Citations
PageRank
Jayant Kumar117311.11
Rohit Prasad246539.06
Huaigu Cao334729.09
Wael Abd-Almageed424824.52
David Doermann54313312.70
Premkumar Natarajan687479.46