Subhadip Basu

ASCORBIC ACID METABOLISM IN DIABETES MELLITUS

In contrast to normal subjects diabetic patients and very low plasma ascorbic acid and significantly high (p less than 0.001) dehydroascorbic acid irrespective of age, sex, duration of the disease, type of treatment, and glycemic control. However, there was no significant difference between the mean leukocyte ascorbate concentrations of the two populations. The in vitro rates of dehydroascorbate reduction in the hemolysate and the erythrocyte reduced glutathione levels and the glucose-6-phosphate dehydrogenase activities, which regulate the dehydroascorbate reduction, were similar in normal and diabetic subjects. The turnover of ascorbic acid was higher in the diabetics than that in the normal volunteers. Experiments with diabetic rats indicated that the increased turnover of ascorbic acid was probably due to increased oxidation of ascorbate to dehydroascorbate in tissue mitochondria. Ascorbic acid supplementation at a dose of 500 mg per day for a brief period of 15 days resulted in an increase in the plasma ascorbate level temporarily, but it did not lower the blood glucose level of the diabetic patients.

Text line extraction from multi-skewed handwritten documents

A novel text line extraction technique is presented for multi-skewed document images of handwritten English or Bengali text. It assumes that hypothetical water flows, from both left and right sides of the image frame, face obstruction from characters of text lines. The stripes of areas left unwetted on the image frame are finally labelled for extraction of text lines. The success rate of the technique, as observed experimentally, are 90.34% and 91.44% for handwritten Bengali and English document images, respectively. The work may contribute significantly for the development of applications related to optical character recognition of Bengali/English text.

A genetic algorithm based region sampling for selection of local features in handwritten digit recognition application

Identification of local regions from where optimal discriminating features can be extracted is one of the major tasks in the area of pattern recognition. To locate such regions different kind of region sampling techniques are used in the literature. There is no standard methodology to identify exactly such regions. Here we have proposed a methodology where local regions of varying heights and widths are created dynamically. Genetic algorithm (GA) is then applied on these local regions to sample the optimal set of local regions from where an optimal feature set can be extracted that has the best discriminating features. We have evaluated the proposed methodology on a data set of handwritten Bangla digits. In the present work, we have randomly generated seven sets of local regions and from every set, GA selects an optimal group of local regions which produces best recognition performance with a support vector machine (SVM) based classifier. Other popular optimization techniques like simulated annealing (SA) and hill climbing (HC) have also been evaluated with the same data set and maximum recognition accuracies were found to be 97%, 96.7% and 96.7% for GA, SA and HC, respectively. We have also compared the performance of the present technique with those of other zone based techniques on the same database.

A hierarchical approach to recognition of handwritten Bangla characters

A novel hierarchical approach is presented here for optical character recognition (OCR) of handwritten Bangla words. Instead of dealing with isolated characters as found in selected works [T.K. Bhowmik, U. Bhattacharya, S.K. Parui, Recognition of Bangla handwritten characters using an MLP classifier based on stroke features, in: Proceedings of the ICONIP, Kolkata, India, 2004, pp. 814-819; K. Roy, U. Pal, F. Kimura, Bangla handwritten character recognition, in: Proceedings of the Second Indian International Conference on Artificial Intelligence (IICAI), 2005, pp. 431-443; S. Basu, N. Das, R. Sarkar, M. Kundu, M. Nasipuri, D.K. Basu, Handwritten Bangla alphabet recognition using an MLP based classifier, in: Proceedings of the Second National Conference on Computer Processing of Bangla, Dhaka, 2005, pp. 285-291; A.F.R. Rahman, R. Rahman, M.C. Fairhurst, Recognition of handwritten Bengali characters: a novel multistage approach, Pattern Recognition 35, 2002, pp. 997-1006; U. Bhattacharya, S.K. Parui, M. Sridhar, F. Kimura, Two-stage recognition of handwritten Bangla alphanumeric characters using neural classifiers, in: Proceedings of the Second Indian International Conference on Artificial Intelligence (IICAI), 2005, pp. 1357-1376; U. Bhattacharya, M. Sridhar, S.K. Parui, On recognition of handwritten Bangla characters, in: Proceedings of the ICVGIP-06, Lecture Notes in Computer Science, vol. 4338, 2006, pp. 817-828], the present approach segments a word image on Matra hierarchy, then recognizes the individual word segments and finally identifies the constituent characters of the word image through intelligent combination of recognition decisions of the associated word segments. Due to possible appearances of consecutive characters of Bangla words on overlapping character positions, segmentation of Bangla word images is not easy. For successful OCR of handwritten Bangla text, not only recognition but also segmentation of word images are important. In this respect the present hierarchical approach deals with both segmentation and recognition of handwritten Bangla word images for a complete solution to handwritten word recognition problem, an essential area of OCR of handwritten Bangla text. In dealing with certain category of word segments, created on Matra hierarchy, a sophisticated recognition technique, viz., two-pass approach [S. Basu, C. Chaudhury, M. Kundu, M. Nasipuri, D.K. Basu, A two pass approach to pattern classification, in: N.R. Pal et al. (Ed.), Lecture Notes in Computer Science, vol. 3316, ICONIP, Kolkata, 2004, pp. 781-786] is employed here. The degree of sophistication of the classification technique is also rationally tuned depending on various categories of word segments to be recognized. For example, the two-pass approach is employed here for recognizing middle zone character segments, whereas recognition of middle zone modified shapes of Bangla script is done through simple template matching. Considering learning and generalization abilities of multi layer perceptrons (MLPs), MLP based pattern classifiers are used here for most of the classification related tasks. A powerful feature set is also designed under this work for recognition of complex character patterns using three types of topological features, viz., longest-run features, modified shadow features and octant-centroid features. In a nutshell, the work deals with a practical problem of OCR of Bangla text involving recognition as well as segmentation of constituent characters of handwritten Bangla words.

A statistical-topological feature combination for recognition of handwritten numerals

Principal Component Analysis (PCA) and Modular PCA (MPCA) are well known statistical methods for recognition of facial images. But only PCA/MPCA is found to be insufficient to achieve high classification accuracy required for handwritten character recognition application. This is due to the shortcomings of those methods to represent certain local morphometric information present in the character patterns. On the other hand Quad-tree based hierarchically derived Longest-Run (QTLR) features, a type of popularly used topological features for character recognition, miss some global statistical information of the characters. In this paper, we have introduced a new combination of PCA/MPCA and QTLR features for OCR of handwritten numerals. The performance of the designed feature-combination is evaluated on handwritten numerals of five popular scripts of Indian sub-continent, viz., Arabic, Bangla, Devanagari, Latin and Telugu with Support Vector Machine (SVM) based classifier. From the results it has been observed that MPCA+QTLR feature combination outperforms PCA+QTLR feature combination and most other conventional features available in the literature.

A novel framework for automatic sorting of postal documents with multi-script address blocks

Recognition of numeric postal codes in a multi-script environment is a classical problem in any postal automation system. In such postal documents, determination of the script of the handwritten postal codes is crucial for subsequent invocation of the digit recognizers for respective scripts. The current framework attempts to infer about the script of the numeric postal code without having any bias from the script of the textual address part of the rest of the address block, as they might differ in a potential multi-script environment. Scope of the current work is to recognize the postal codes written in any of the four popular scripts, viz., Latin, Devanagari, Bangla and Urdu. For this purpose, we first implement a Hough transformation based technique to localize the postal-code blocks from structured postal documents with defined address block region. Isolated handwritten digit patterns are then extracted from the localized postal-code region. In the next stage of the developed framework, similar shaped digit patterns of the said four scripts are grouped in 25 clusters. A script independent unified pattern classifier is then designed to classify the numeric postal codes into one of these 25 clusters. Based on these classification decisions a rule-based script inference engine is designed to infer about the script of the numeric postal code. One of the four script specific classifiers is subsequently invoked to recognize the digit patterns of the corresponding script. A novel quad-tree based image partitioning technique is also developed in this work for effective feature extraction from the numeric digit patterns. The average recognition accuracy over ten-fold cross validation of results for the support vector machine (SVM) based 25-class unified pattern classifier is obtained as 92.03%. With randomly selected six-digit numeric strings of four different scripts; an average of 96.72% script inference accuracy is achieved. The average of tenfold cross-validation recognition accuracies of the individual SVM classifiers for the Latin, Devanagari, Bangla and Urdu numerals are observed as 95.55%, 95.63%, 97.15% and 96.20%, respectively.

PPI_SVM: Prediction of protein-protein interactions using machine learning, domain-domain affinities and frequency tables

Protein-protein interactions (PPI) control most of the biological processes in a living cell. In order to fully understand protein functions, a knowledge of protein-protein interactions is necessary. Prediction of PPI is challenging, especially when the three-dimensional structure of interacting partners is not known. Recently, a novel prediction method was proposed by exploiting physical interactions of constituent domains. We propose here a novel knowledge-based prediction method, namely PPI_SVM, which predicts interactions between two protein sequences by exploiting their domain information. We trained a two-class support vector machine on the benchmarking set of pairs of interacting proteins extracted from the Database of Interacting Proteins (DIP). The method considers all possible combinations of constituent domains between two protein sequences, unlike most of the existing approaches. Moreover, it deals with both single-domain proteins and multi domain proteins; therefore it can be applied to the whole proteome in high-throughput studies. Our machine learning classifier, following a brainstorming approach, achieves accuracy of 86%, with specificity of 95%, and sensitivity of 75%, which are better results than most previous methods that sacrifice recall values in order to boost the overall precision. Our method has on average better sensitivity combined with good selectivity on the benchmarking dataset. The PPI_SVM source code, train/test datasets and supplementary files are available freely in the public domain at: http://code.google.com/p/cmater-bioinfo/.

A Fuzzy Technique for Segmentation of Handwritten Bangla Word Images

A fuzzy technique for segmentation of handwritten Bangla word images is presented. It works in two steps. In first step, the black pixels constituting the Matra (i.e., the longest horizontal line joining the tops of individual characters of a Bangla word) in the target word image is identified by using a fuzzy feature. In second step, some of the black pixels on the Matra are identified as segment points (i.e., the points through which the word is to be segmented) by using three fuzzy features. On experimentation with a set of 210 samples of handwritten Bangla words, collected from different sources, the average success rate of the technique is shown to be 95.32%. Apart from certain limitations, the technique can be considered as a significant step towards the development of a full-fledged Bangla OCR system, especially for handwritten documents

PSP_MCSVM: brainstorming consensus prediction of protein secondary structures using two-stage multiclass support vector machines.

Secondary structure prediction is a crucial task for understanding the variety of protein structures and performed biological functions. Prediction of secondary structures for new proteins using their amino acid sequences is of fundamental importance in bioinformatics. We propose a novel technique to predict protein secondary structures based on position-specific scoring matrices (PSSMs) and physico-chemical properties of amino acids. It is a two stage approach involving multiclass support vector machines (SVMs) as classifiers for three different structural conformations, viz., helix, sheet and coil. In the first stage, PSSMs obtained from PSI-BLAST and five specially selected physicochemical properties of amino acids are fed into SVMs as features for sequence-to-structure prediction. Confidence values for forming helix, sheet and coil that are obtained from the first stage SVM are then used in the second stage SVM for performing structure-to-structure prediction. The two-stage cascaded classifiers (PSP_MCSVM) are trained with proteins from RS126 dataset. The classifiers are finally tested on target proteins of critical assessment of protein structure prediction experiment-9 (CASP9). PSP_MCSVM with brainstorming consensus procedure performs better than the prediction servers like Predator, DSC, SIMPA96, for randomly selected proteins from CASP9 targets. The overall performance is found to be comparable with the current state-of-the art. PSP_MCSVM source code, train-test datasets and supplementary files are available freely in public domain at: http://sysbio.icm.edu.pl/secstruct and http://code.google.com/p/cmater-bioinfo/

Text Line Segmentation for Unconstrained Handwritten Document Images Using Neighborhood Connected Component Analysis

Text line extraction is the first and one of the most critical steps in optical character recognition (OCR) of unconstrained handwritten documents. The present work reports a new methodology based on comparison of neighborhood connected components to determine whether they belong to the same text line. Components which are very small or very large compared to the average component height are ignored in the preprocessing step. During post-processing, such components are reconsidered and allocated to the lines to which they most suitably belong. The performance of the developed technique is evaluated on the benchmark training dataset for the ICDAR 2009 handwriting segmentation contest. The dataset consists of English, French, German and Greek handwritten texts. The overall text line identification accuracy on the mentioned dataset is observed to be around 93.35%.