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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 Our experimental results on Arabic, Chinese, and English historical documents show that our approach manage to separate multi-skew text blocks into lines at high success rates.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.

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In this paper, we present a multi-level recognizer for online Arabic handwriting. In Arabic script (handwritten and printed)

The difficulties in segmenting cursive words into individual characters have shifted the focus of handwriting recognition research from segmentation-based approaches to segmentation-free (holistic) methods. However, maintaining and training large number of prototypes (models) that represent the words in the dictionary make the training process extremely expensive and difficult in computing resources. In this paper we present an efficient system that automatically generates prototypes for each word in a given dictionary using multiple appearance of each letter shape. Multiple appearance allows for many permutation of shapes for each word and thus complicates searching for the right prototype. To simplify the training, reduce the maintained prototypes, and avoid over fitting, we used dimensionality reduction followed by clustering techniques to reduce the size of these sets without affecting their ability to represent the wide variations of the handwriting styles. A set of generated fonts are created by professional writers imitating all handwriting styles for each character in each position. These Fonts are used to generate all shapes for writing each word-part in a comprehensive dictionary. Principal component analysis and k-means clustering techniques are performed to select the minimal number of shapes representing the wide variations of handwriting styles for a word-part. Experimental results using an online recognition system proves the credibility of this process compared to manually generated databases.