Markov Random Field Based Text Identification from Annotated Machine Printed Documents

Author

Peng, Xujun ; Setlur, Srirangaraj ; Govindaraju, Venu ; Sitaram, Ramachandrula ; Bhuvanagiri, Kiran

Author_Institution

Dept. of Comput. Sci. & Eng., SUNY at Buffalo, Amherst, NY, USA

fYear

2009

Firstpage

431

Lastpage

435

Abstract

In this paper, we describe an approach to segment handwritten text, machine printed text and noise from annotated machine printed documents. Three categories of word level features are extracted. We use a modified K-Means clustering algorithm for classification followed by a relabeling procedure using Markov Random Field(MRF) based on a concept of neighboring patches and Belief Propagation(BP) rules. Experimental results on an imbalanced data set show that our approach achieves an overall recall of 96.33%.

Keywords

Markov processes; document image processing; feature extraction; image classification; image segmentation; pattern clustering; random processes; text analysis; Markov random field; annotated machine printed document; belief propagation; feature extraction; k-mean clustering algorithm; machine printed text; segment handwritten text; text identification; Classification algorithms; Feature extraction; Gabor filters; Handwriting recognition; Hidden Markov models; Image segmentation; Markov random fields; Optical character recognition software; Text analysis; Text recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Document Analysis and Recognition, 2009. ICDAR '09. 10th International Conference on

Conference_Location

Barcelona

ISSN

1520-5363

Print_ISBN

978-1-4244-4500-4

Electronic_ISBN

1520-5363

Type

conf

DOI

10.1109/ICDAR.2009.237

Filename

5277639