Semi-supervised learning for text-line detection
Automatically detecting text-lines from document images has been long studied. However, most researchers today are focusing on boosting the detection rate instead of noise removal. In this paper, we propose a semi-supervised learning framework that targets to segment Manhattan-layout documents with significant levels of noise. The algorithm consists of three steps: first, an initial segmentation process uses the seed filling algorithm; second, an iterative grouping process uses the projection profiles to estimate the vertical border of page contents; third, an inside page-content noise removal uses the online training and classification. We test our algorithm using two databases. The first is the University of Washington (UW)-III database with 1,600 images of different input qualities that has been widely used by the Document Analysis Research (DAR) communities to measure segmentation algorithm performance. The second is the NILE database created by sampling from 320 journals pages of east Asian, east European and middle Eastern languages. The result shows that our framework achieves competitive performance in terms of both page frame level segmentation and text-line level segmentation, and is particularly strong at filtering noise. It also shows that our algorithm is more adaptive to language variations.
semi-supervised learning text-line detection document segmentation language adaptiveness