8710639
Adaptive Hindi OCR using generalized Hausdorff image comparison
We present an adaptive Hindi OCR implemented as part of a rapidly retargetable language tool effort. The system includes: script identification, character segmentation, training sample creation, and character recognition. In script identification, Hindi words are identified from bilingual or multilingual documents based on features of the Devanagari script or using Support Vector Machines. Identified words are then segmented into individual characters in the next step, where the composite characters are identified and further segmented based on the structural properties of the script and statistical information. Segmented characters are recognized using generalized Hausdorff image comparison (GHIC) and postprocessing is applied to improve the performance. The OCR system, which was designed and implemented in one month, was applied to a complete Hindi--English bilingual dictionary and a set of ideal images extracted from Hindi documents in PDF format. Experimental results show the recognition accuracy can reach 88% for noisy images and 95% for ideal images. The presented method can also be extended to design OCR systems for different scripts.
14752983
Gabor filter based multi-class classifier for scanned document images
When scanning documents with a large number of pagessuch as books, it is often feasible to provide a minimalnumber of training samples to personalize the system tocompensate for global shifts in how the document wascreated or in scanning parameters. In this paper, wepresent a supervised multi-class classifier based onGabor filters that is used to classify the scripts, font-faces,and font-styles (bold, italic, normal etc.) in anapplication where the classes are known. Classificationis performed at the word level (glyphs separated by whitespace) given training samples of each class. This methodwas applied to a variety of bilingual dictionaries toidentify different scripts, and simultaneously, to classifyRoman scripts into bold, italic and normal font-styles.Experimental results show the effectiveness of thisapproach in increasing performance over classifierstrained for general documents.
6752301
The Connectivity Structure, Giant Strong Component and Centrality of Metabolic Networks
MOTIVATION
Structural and functional analysis of genome-based large-scale metabolic networks is important for understanding the design principles and regulation of the metabolism at a system level. The metabolic network is conventionally considered to be highly integrated and very complex. A rational reduction of the metabolic network to its core structure and a deeper understanding of its functional modules are important.


RESULTS
In this work, we show that the metabolites in a metabolic network are far from fully connected. A connectivity structure consisting of four major subsets of metabolites and reactions, i.e. a fully connected sub-network, a substrate subset, a product subset and an isolated subset is found to exist in metabolic networks of 65 fully sequenced organisms. The largest fully connected part of a metabolic network, called 'the giant strong component (GSC)', represents the most complicated part and the core of the network and has the feature of scale-free networks. The average path length of the whole network is primarily determined by that of the GSC. For most of the organisms, GSC normally contains less than one-third of the nodes of the network. This connectivity structure is very similar to the 'bow-tie' structure of World Wide Web. Our results indicate that the bow-tie structure may be common for large-scale directed networks. More importantly, the uncovered structure feature makes a structural and functional analysis of large-scale metabolic network more amenable. As shown in this work, comparing the closeness centrality of the nodes in the GSC can identify the most central metabolites of a metabolic network. To quantitatively characterize the overall connection structure of the GSC we introduced the term 'overall closeness centralization index (OCCI)'. OCCI correlates well with the average path length of the GSC and is a useful parameter for a system-level comparison of metabolic networks of different organisms.


SUPPLEMENTARY INFORMATION
http://genome.gbf.de/bioinformatics/