In this paper, we present a method to extract the text lines in poorly structured documents. The text lines may have
different orientations, considerably curved shapes, and there are possibly a few wide inter-word gaps in a text line. Those
text lines can be found in posters, blocks of addresses, artistic documents. Our method is an expansion of the traditional
perceptual grouping. We develop novel solutions to overcome the problems of insufficient seed points and varied
orientations in a single line. In this paper, we assume that text lines consists of connected components, in which each
connected components is a set of black pixels within a letter, or some touched letters. In our scheme, the connected
components closer than an iteratively incremented threshold will be combined to make chains of connected components.
Elongate chains are identified as the seed chains of lines. Then the seed chains are extended to the left and the right
regarding the local orientations. The local orientations will be reevaluated at each side of the chains when it is extended.
By this process, all text lines are finally constructed. The advantage of the proposed method over prior works in
extraction of curved text lines is that this method can both deal with more than a specific language and extract text lines
containing some wide inter-word gaps. The proposed method is good for extraction of the considerably curved text lines
from logos and slogans in our experiment; 98% and 94% for the straight-line extraction and the curved-line extraction,