Data Mining Practical Machine Learning Tools and Techniques

In India, more than 300 million people use Devanagari script for documentation. There has been a significant improvement in the research related to the recognition of printed as well as handwritten Devanagari text in the past few years. State of the art from 1970s of machine printed and handwritten Devanagari optical character recognition (OCR) is discussed in this paper. All feature-extraction techniques as well as training, classification and matching techniques useful for the recognition are discussed in various sections of the paper. An attempt is made to address the most important results reported so far and it is also tried to highlight the beneficial directions of the research till date. Moreover, the paper also contains a comprehensive bibliography of many selected papers appeared in reputed journals and conference proceedings as an aid for the researchers working in the field of Devanagari OCR.

Offline Recognition of Devanagari Script: A Survey

Spread spectrum systems

We survey the optical character recognition (OCR) literature with reference to the Urdu-like cursive scripts. In particular, the Urdu, Pushto, and Sindhi languages are discussed, with the emphasis being on the Nasta'liq and Naskh scripts. Before detaining the OCR works, the peculiarities of the Urdu-like scripts are outlined, which are followed by the presentation of the available text image databases. For the sake of clarity, the various attempts are grouped into three parts, namely: (a) printed, (b) handwritten, and (c) online character recognition. Within each part, the works are analyzed par rapport a typical OCR pipeline with an emphasis on the preprocessing, segmentation, feature extraction, classification, and recognition. HighlightsA literature review of the Nasta'liq and Naskh cursive script OCR.The peculiarities and challenges are described a priori.Printed, handwritten and online OCR efforts are being explored.Analyses based on the stages of a typical OCR pipeline.

The optical character recognition of Urdu-like cursive scripts

Prediction and optimization of surface roughness and micro-hardness using grnn and MOORA-fuzzy-a MCDM approach for nitinol in WEDM

In this paper, two types of Artificial Neural Network (ANNs), Generalized Regression Neural Network (GRNN) and Radial Basis Function (RBF) have been used for heart disease to prescribe the medicine. Diagnosing the heart disease and prescribing the medicine on the basis of symptoms is a very challenging task to improve the ability of the physicians. The training capacity and medicines provided by these two techniques are compared with the original medicines provided by the heart specialist. About 300 patients data are collected from Sahara Hospital, Aurangabad under the supervision of doctor. This study includes the detailed information about patient and preprocessing was done. The GRNN and RBF have been applied over this patient data for the outcome the medicine. The result of these evaluation show that the overall performance of RBF can be applied successfully for prescribing the medicine for the heart disease patient.

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Generalized Regression Neural Network and Radial Basis Function for Heart Disease Diagnosis

This paper presents an experimental evaluation of the effectiveness of various techniques based upon moment invariants (Hu, 1961). The features that have been extracted are based on moments, image partition, principal component axes (PCA), correlation coefficient and perturbed moments. In image partition method, the image is divided into four parts with three different ways. The principal component axes (PCA) method has been used to balance the distribution of pixels in different regions of the image. Correlation coefficient provides dependencies of different moments on each other. In perturbed moment method, moment invariants are computed by small perturbation in image and information is extracted from the perturbation. All techniques have been applied on 2000 handwritten Devanagari numerals. The Gaussian distribution function has been adopted for classification. The success rate can be enhanced to 92% using image partition method

Feature Extraction Based on Moment Invariants for Handwriting Recognition

In this paper, a system based Handwritten Devanagari Character Recognition (HDCR) is proposed. The paper presents an experimental assessment of the efficiency of various methods based on Invariant Moments for handwritten devanagari vowels recognition. The technique is independent of size, slant, orientation, translation and other variations in handwritten vowels. For segmentation of the devanagari words, the header line (Shirorekha) , plays vital role. The same tool with vertical and horizontal projection has been adapted to isolate the 13 vowels in five different groups. In order to enhance the performance of the system, an attempt has been made to compute invariant moments by small perturbation in image and information is extracted from the perturbation. But it was found that, another local feature descriptor, image partition in different zoning is better representation of the features than perturbation. The other method of image partition with different ways found better. 10 samples of each vowel from 25 people have been sampled and a database was prepared. Individual image is normalized to 40X40 pixel size. The Fuzzy Gaussian Membership function has been adopted for classification. The success rate of the method is found to be 94.56.

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Invariant Moments Based Feature Extraction for Handwritten Devanagari Vowels Recognition

Artificial Neural Network (ANN) are one of the recently explored advanced technologies which shows promise in the area of medical. In this paper, Radial Basis Function is used to predict the medical prescription of heart disease. This work includes the detailed information about the patient's symptoms and preprocessing was done. The trainee doctors can also use this web based tool for diagnosis and appropriate medical prescription of heart disease using radial basis function. About 300 patient's data were collected from Sahara Hospital, Aurangabad under the supervision of heart specialist. The radial basis function is applied to heart disease data for prediction of medical prescription of heart disease. Results obtained show that radial basis function can be successfully used for prescribing the medicines for heart disease. The role of effective diagnosis and the advantages of data training on neural networks-based automatic medical diagnosis system are suggested by the outcomes.

Prediction of heart disease medical prescription using radial basis function

Expert system are used extensively in many domains. Heart disease diagnosis is a complicated process and requires high level of expertise. This paper describes aiming to develop a expert system for diagnosing of heart disease using support vector machine and feedforward backpropagation technique. Now a days neural network are being used successfully in an increasing number of application areas. This work includes the detailed information about patient and preprocessing was done. The Support Vector Machine (SVM) and feedforward Backpropagation technique have been applied over the data for the expert system. To make the system more authentic and reliable out of 300 patients 250 patients were used for training set and 50 for evaluation process. In conclusion, we have used two neural network techniques but we are getting just 50% to 60% output i.e. not reliable for the patient. This expert system data can also be applied to improve the accuracy the medicine using some other neural network techniques.

R. J. Ramteke

Papers

Generalized Regression Neural Network and Radial Basis Function for Heart Disease Diagnosis

Feature Extraction Based on Moment Invariants for Handwriting Recognition

Invariant Moments Based Feature Extraction for Handwritten Devanagari Vowels Recognition

Prediction of heart disease medical prescription using radial basis function

Diagnosis and Medical Prescription of Heart Disease Using Support Vector Machine and Feedforward Backpropagation Technique