Segmentation of Curled Textlines Using Active Contours

Abstract

Segmentation of curled textlines from warped document images is one of the major issues in document image dewarping. Most of the curled textlines segmentation algorithms present in the literature today are sensitive to the degree of curl, direction of curl, and spacing between adjacent lines. We present a new algorithm for curled textline segmentation which is robust to above mentioned problems at the expense of high execution time. We will demonstrate this insensitivity in a performance evaluation section. Our approach is based on the state-of-the-art image segmentation technique: Active Contour Model (Snake) with the novel idea of several baby snakes and their convergence in a vertical direction only. Experiment on publically available CBDAR 2007 document image dewarping contest dataset shows our textline segmentation algorithm accuracy of 97.96%.

Textline information extraction from grayscale camera-captured document images

“…This section introduces the modified snakes model for estimating the information of x-line and baseline pairs from detected textlines. Our baby-snake [7] and snakelet [8] models are also based on snakes for curled textline detection, but from binarized document image. The modified snake model presented here is as an extension of [7,8].…”

“…Our baby-snake [7] and snakelet [8] models are also based on snakes for curled textline detection, but from binarized document image. The modified snake model presented here is as an extension of [7,8]. The features of modified snakes model for estimating x-line and baseline pairs are described below:…”

“…Previous approaches of curled textline detection [1,2,3,4,5,6,7,8] work on binarized images. These approaches can be divided into two categories: (a) heuristic search [1,2,3,4,5,6] and (b) active contours (snakes) [7,8].…”

“…Most of these approaches use rule-based criteria for textline searching. Our active contours (snakes) based baby-snakes [7] model introduced the use of many small open curved snakes and snakelets [8] model introduced many small growing coupled snakes pairs for curled textlines detection from binarized images.…”

Neural Networks for Document Image and Text Processing

“…[Nikolaou et al 2010] applies a modified RLSA by adding CCs, white spaces, punctuation marks, and skeleton of the strokes knowledge to the smearing process. In [Bukhari et al 2008[Bukhari et al , 2009] the authors tackle the text segmentation problem by using active contours (snakes) as a base unit to minimize the energy function. The final lines are extracted by joining neighboring snakes after applying several deformations until they stick together.…”