Mita Nasipuri

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.

Combining Multiple Feature Extraction Techniques for Handwritten Devnagari Character Recognition

In this paper, we present an OCR for handwritten Devnagari characters. Basic symbols are recognized by neural classifier. We have used four feature extraction techniques namely, intersection, shadow feature, chain code histogram and straight line fitting features. Shadow features are computed globally for character image while intersection features, chain code histogram features and line fitting features are computed by dividing the character image into different segments. Weighted majority voting technique is used for combining the classification decision obtained from four multi layer perceptron(MLP) based classifier. On experimentation with a dataset of 4900 samples the overall recognition rate observed is 92.80% as we considered top five choices results. This method is compared with other recent methods for handwritten Devnagari character recognition and it has been observed that this approach has better success rate than other methods.

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.

Knowledge-based ECG interpretation: a critical review

Abstract This work presents a brief review of some selected knowledge-based approaches to electrocardiographic (ECG) pattern interpretation for diagnosing various malfunctions of the human heart. The knowledge-based approaches discussed here include modeling an ECG pattern through an AND/OR graph, a rule-based approach and a procedural semantic network (PSN) based approach for ECG interpretation. However, certain syntactic approaches to ECG interpretation are also covered, considering their precursory roles to knowledge-based ECG interpretation. A fuzzy-logic-based approach is included in the discussion to show how imprecision can be dealt with in modeling cardiological knowledge. A domain-dependent control algorithm is discussed to show how the production level parallelism can be exploited to reduce the length of the match–resolve–act cycle of a rule based ECG interpretation system. The review also contains a brief description of some recent applications of connectionist approaches to ECG interpretation. This discussion finally ends with a comparative assessment of performances of all the above-mentioned knowledge-based approaches to ECG interpretation and some hints about the future directions of work in this field.

Face recognition using point symmetry distance-based RBF network

In this paper, a face recognition technique using a radial basis function neural network (RBFNN) is presented. The centers of the hidden layer units of the RBFNN are selected by using a heuristic approach and point symmetry distance as similarity measure. The performance of the present method has been evaluated using the ATT first with no rejection criteria, and then with rejection criteria. The experimental results show that the present method achieves excellent performance, both in terms of recognition rates and learning efficiency. The average recognition rates, as obtained using 10 different permutations of 1, 3 and 5 training images per subject are 76.06, 92.61 and 97.20%, respectively, when tested without any rejection criteria. On the other hand, by imposing rejection criteria, the average recognition rates of the system become 99.34, 99.80 and 99.93%, respectively, for the above permutations of the training images. The system recognizes a face within about 22ms on a low-cost computing system with a 450MHz P-III processor, and thereby extending its capability to identify faces in interframe periods of video and in real time.

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.

A knowledge-based approach to ECG interpretation using fuzzy logic

A rule-based expert system which uses generalized modus ponens (GMP) from fuzzy logic as a rule of inference is described here for classification of abnormalities related to rhythm disorder in the human heart, through interpretation of the patients electrocardiographic (EGG) patterns. Application of GMP makes diagnosis of a wide range of variations in the input ECG patterns possible even if they differ from the patterns defined in the preconditions of the rules of the rulebase. The work shows how fuzzy logic with suitably drawn possibility distributions of variables of cardiological domain plays a significant role in making the expert system sensitive to finer variations of input ECG patterns, which are very common in bioelectric signals, without enhancing the size of the rulebase.

Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images

A conditional spatial fuzzy C-means (csFCM) clustering algorithm to improve the robustness of the conventional FCM algorithm is presented.The method incorporates conditional affects and spatial information into the membership functions.The algorithm resolves the problem of sensitivity to noise and intensity inhomogeneity in magnetic resonance imaging (MRI) data.The experimental results on four volumes of simulated and one volume of real-patient MRI brain images, each one having 51 images, support efficiency of the csFCM algorithm.The csFCM algorithm has superior performance in terms of qualitative and quantitative studies on the image segmentation results than the k-means, FCM and some other recently proposed FCM-based algorithms. The fuzzy C-means (FCM) algorithm has got significant importance due to its unsupervised form of learning and more tolerant to variations and noise as compared to other methods in medical image segmentation. In this paper, we propose a conditional spatial fuzzy C-means (csFCM) clustering algorithm to improve the robustness of the conventional FCM algorithm. This is achieved through the incorporation of conditioning effects imposed by an auxiliary (conditional) variable corresponding to each pixel, which describes a level of involvement of the pixel in the constructed clusters, and spatial information into the membership functions. The problem of sensitivity to noise and intensity inhomogeneity in magnetic resonance imaging (MRI) data is effectively reduced by incorporating local and global spatial information into a weighted membership function. The experimental results on four volumes of simulated and one volume of real-patient MRI brain images, each one having 51 images, show that the csFCM algorithm has superior performance in terms of qualitative and quantitative studies such as, cluster validity functions, segmentation accuracy, tissue segmentation accuracy and receiver operating characteristic (ROC) curve on the image segmentation results than the k-means, FCM and some other recently proposed FCM-based algorithms.