. Effective text region extraction and binarization of image embedded text documents on mobile devices having limited computational resources is an open research problem. In this paper, we present one such technique for preprocessing images captured with built-in cameras of handheld devices with an aim of developing an efficient Business Card Reader. At first, the card image is processed for isolating foreground components. These foreground components are classified as either text or non-text using different feature descriptors of texts and images. (...) The non-text components are removed and the textual ones are binarized with a fast adaptive algorithm. Specifically, we propose new techniques for foreground component isolation, text extraction and binarization of text regions from camera captured business card images. Experiments with business card images of various resolutions show that the present technique yields better accuracy and involves low computational overhead in comparison with the state-of-the-art. We achieve optimum text/non-text separation performance with images of resolution 800×600 pixels with an average recall rate of 93.90% and a precision rate of 96.84%. It involves a peak memory consumption of 0.68 MB and processing time of 0.102 seconds on a moderately powerful notebook, and 4 seconds of processing time on a PDA. (shrink)

A novel piecewise water flow technique for text line extraction from multi-skewed document images of handwritten text of different scripts is presented here. The basic water flow technique assumes that the hypothetical water flows from both left and right sides of the image frame. This flow of water fills up the gaps between consecutive objects but faces obstruction if any object lies in the path of the flow. All unwetted regions in the document image are then labeled distinctly to extract (...) the text lines. However, the technique fails when two neighboring text lines touch each other, as water gets obstructed by the touching segment. To get rid of this difficulty, we have modified the basic water flow technique by iteratively applying the same over the vertically segmented document images. The main purpose of this vertical segmentation is to localize the text line segment where two text lines get joined. These segments are then horizontally fragmented, and each fragment is placed suitably to the text line in which it actually belongs to. This way, the probable data loss during isolation of the touching text line segment is minimized. Both the techniques have been tested on three different databases, viz., CMATERdb 1.1.1, CMATERdb 1.1.2, and ICDAR2009 handwritten segmentation contest pages, respectively. The test results show that the present technique outperforms the basic one for all three databases. (shrink)

In this paper, a novel approach for word extraction and character segmentation from the handwritten Bangla document images is reported. At first, a modified Run Length Smoothing Algorithm, called Spiral Run Length Smearing Algorithm, is applied for the extraction of words from the text lines of unconstrained handwritten Bangla document images. This technique has helped to overcome some of the drawbacks of standard horizontal and vertical RLSA techniques. SRLSA technique has been applied on the Bangla handwritten document image database CMATERdb1.1.1 (...) and the success rate of the word extraction is found to be 86.01%. In the second part of the work, we have presented a useful solution to the problem on how best word images of handwritten Bangla script can be segmented into constituent characters. Moreover, the technique can segment the words having discontinuity in Matra, a prominent feature of Bangla script. It also optimizes the trade-off between under/over segmentation as Matra region and segmentation points are estimated more precisely. As a result, better word segmentation accuracy is achieved with minimal data loss. Here, a success rate of 92.48% is observed on a dataset of 750 handwritten Bangla words which is 3.35% higher than that of our earlier techniques. (shrink)