Language-Independent Text Lines Extraction Using Seam Carving

Abstract

In this paper, we present a novel languageindependent algorithm for extracting text-lines from handwritten document images. Our algorithm is based on the seam carving approach for content aware image resizing. We adopted the signed distance transform to generate the energy map, where extreme points indicate the layout of text-lines. Dynamic programming is then used to compute the minimum energy left-to-right paths (seams), which pass along the "middle" of the text-lines. Each path intersects a set of components, which determine the extracted text-line and estimate its hight. The estimated hight determines the text-line's region, which guides splitting touching components among consecutive lines. Unassigned components that fall within the region of a text-line are added to the components list of the line. The components between two consecutive lines are processed when the two lines are extracted and assigned to the closest text-line, based on the attributes of extracted lines, the sizes and positions of components. Our experimental results on Arabic, Chinese, and English historical documents show that our approach manage to separate multi-skew text blocks into lines at high success rates.

User-assisted alignment of Arabic historical manuscripts

“…We have adopted the method recently developed by Saabni and El-Sana et al [30] to extract text lines from the compared documents and generate a set of images that represent the rows of the documents. This line extraction method, which is based on Seam Carving framework [31], computes an energy map of the input text block image and determines the seams that pass across text lines.…”

Text line segmentation for gray scale historical document images

“…Tracking minima points to follow the white-most and black-most pixels along horizontal paths are used to estimate the boundaries and baselines of text lines [22]. Seam carving approach is used to find the seams, which resemble the baseline of text row, using a signed-distance-transform based energy map [8]. Various grouping techniques, such as heuristic rules, learning algorithms, nearest neighbor, and search trees are applied to segment text-line in document images [23,24,25,26,27,28,29].…”

“…Saabni and ElSana [8] presented an interesting line extraction algorithm, which is based on the seam carving technique introduced by Avidan and Shamir [9]. Their algorithm is limited to binary images and as a result it inherits the limitations of image binarization, which introduce noise and various artifacts.…”

“…The medial and separating seams propagate according to different energy maps, which are defined based on the constructed distance transform. The inability to determine the boundaries of text lines forces Saabni and El-Sana [8] to recompute the energy map for the entire page image after the extraction of each text line. The separating seams determine the text line boundaries, define the region to be updated, and overcome the limitation of recomputing the energy map.…”

Neural Networks for Document Image and Text Processing

“…The base idea behind applying it to TLE is to determine the energy function that will found spaces between lines. [Saabni et al 2011] use the signed distance transform to generate the energy map. [Arvanitopoulos et al 2014] apply a modification of the seam carving procedure with text line constraints.…”