No 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 Our experimental results on Arabic, Chinese, and English historical documents show that our approach manage to separate multi-skew text blocks into lines at high success rates.

Arabic script is naturally cursive and unconstrained and, as a result, an automatic recognition of its handwriting is a challenging problem. The analysis of Arabic script is further complicated in comparison to Latin script due to obligatory dots/stokes that are placed above or below most letters. In this paper, we introduce a new approach that performs online Arabic word recognition on a continuous word-part level, while performing training on the letter level. In addition, we appropriately handle delayed strokes by first detecting them and then integrating them into the word-part body. Our current implementation is based on Hidden Markov Models (HMM) and correctly handles most of the Arabic script recognition difficulties. We have tested our implementation using various dictionaries and multiple writers and have achieved encouraging results for both writer-dependent and writer-independent recognition.