A New Text Detection Algorithm for Content-Oriented Line Drawing Image Retrieval
Content retrieval of scanned line drawing images is a difficult problem, especially from real-life large scale databases. Existing algorithms don’t work well due to their low efficiency by first recognizing various types of graphical primitives and then content-oriented texts. A new method for directly detecting texts from line drawing images is proposed in this paper. We first decompose a drawing image into a set of Local Consecutive Segments (LCSs). A LCS is defined as a minimum meaningful structural unit to imitate a stroke during human-drawing process. Next, we identify candidate character LCSs by statistical analysis and merge them into character LCS blocks by geometrical analysis. Finally, Hough transforms are applied to calculate the orientations of character LCS blocks and generate candidate strings. Experimental results show that our algorithm can well detect strings in any orientation. Our method is robust to text-graphic touching, scanning degradation and drawing noises, providing an efficient approach for content retrieval of document images.
Keywordstext detection content-oriented line drawings image retrieval
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