Text Identification in Noisy Document Images Using Markov Random Field
نویسندگان
چکیده
In this paper we address the problem of the identification of text from noisy documents. We segment and identify handwriting from machine printed text because 1) handwriting in a document often indicates corrections, additions or other supplemental information that should be treated differently from the main or body content, and 2) the segmentation and recognition techniques for machine printed text and handwriting are significantly different. Our novelty is that we treat noise as a separate class and model noise based on selected features. Trained Fisher classifiers are used to identify machine printed text and handwriting from noise. We further exploit context to refine the classification. A Markov Random Field (MRF) based approach is used to model the geometrical structure of the printed text, handwriting and noise to rectify the mis-classification. Experimental results show our approach is promising and robust, and can significantly improve the page segmentation results in noise documents.
منابع مشابه
Handwriting Identification , Matching , and Indexing in Noisy
Title of dissertation: HANDWRITING IDENTIFICATION, MATCHING, AND INDEXING IN NOISY DOCUMENT IMAGES Yefeng Zheng, Doctor of Philosophy, 2005 Dissertation directed by: Professor Rama Chellappa Department of Electrical and Computer Engineering Throughout history, handwriting has been the primary means of recording information that is persevered across both time and space. With the coming of the el...
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