• MUNISH KUMAR

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    • A novel framework for writer identification based on pre-segmented Gurmukhi characters

      MUNISH KUMAR M K JINDAL R K SHARMA SIMPEL RANI JINDAL

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      Handwriting is an obtained apparatus utilized for correspondence of one’s recognition or sentiments. Components that judge a person’s handwriting is not merely subject to the individual’s handwriting depends on the background, additionally considers like nervousness, inspiration and the reason for the handwriting. In spite of the high variation, in a man’s handwriting, recent outcomes from various writers have demonstrated that it has adequate individual quality to be utilized as an identification strategy. In this paper, theauthors are the pact with a novel approach to text dependent writer identification in view of pre-segmented Gurmukhi characters. The text dependent writer identification framework proposed in this paper includes distinctive stages like preprocessing, feature extraction, classification or identification. The feature extraction stage incorporates four schemes, zoning, diagonal, transitions and peak extent based features. To analyze the proposed framework execution, experiments are performed with two classifiers, namely, k-NN and SVM. SVM is also considered with linear-kernel in the present work. For experimental results, we have collected 31,500 samples from 90 different writers for 35 class problem. Maximum writer identification accuracy of 89.85% has been achieved by using a combination of zoning, transition and peak extent based features with Linear-SVMclassifier when we have taken 70% data as the training set and remaining 30% data as the testing set. Using 10-fold cross validation, we have achieved an accuracy of 94.76% with a combination of zoning, transition andpeak extent based features and Linear-SVM classifier.

    • Devanagari ancient documents recognition using statistical feature extraction techniques

      SONIKA NARANG M K JINDAL MUNISH KUMAR

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      Devanagari ancient document recognition process is drawing a lot of consideration from researchers nowadays. These ancient documents contain a wealth of knowledge. However, these documents are not available to all because of their fragile condition. A Devanagari ancient manuscript recognition system isdesigned for digital archiving. This system includes image binarization, character segmentation and recognition phases. It incorporates automatic recognition of scanned and segmented characters. Segmented characters mayinclude basic characters (vowels and consonants), modifiers (matras) and various compound characters (characters formed by joining more than one basic characters). In this paper, handwritten Devanagari ancient manuscripts recognition system has been presented using statistical features extraction techniques. In feature extraction phase, intersection points, open endpoints, centroid, horizontal peak extent and vertical peak extent features are extracted. For classification, Convolutional Neural Network, Neural Network, Multilayer Perceptron,RBF-SVM and random forest techniques are considered in this work. Various feature extraction and classification techniques are considered and compared to the recognition of basic characters segmented from Devanagari ancient manuscripts. A data set, of 6152 pre-segmented samples of Devanagari ancient documents, is considered for experimental work. Authors have achieved 88.95% recognition accuracy using a combination of all features and a combination of all classifiers considered in this work by a simple majority voting scheme.

    • Distortion, rotation and scale invariant recognition of hollow Hindi characters

      MOHINDER KUMAR M K JINDAL MUNISH KUMAR

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      Computer vision is a very vast concept and a lot of researchers are inventing new ideas to improve recognition accuracy for the machine. Today one can see driverless cars, automatic robots doing many humanlike activities like playing, dancing, and even in defence service. With the invention of machine learning and deep learning models, the ability of prediction is also improving drastically. The ability of machines to understand printed or handwritten text is also improved these days. Accurate software tools are available for text recognition. The recognition of optical characters is a very mature concept in the Roman script but is in the developing stage for Devanagari script. These OCR systems are producing accurate results in clean printing but perform very poorly when the printing quality is not up to the mark. The performance is even degraded when the characters are distorted or very badly printed/scanned. We have collected 3900 distorted Hindi characters. These characters are hollow in style and randomly rotated and highly distorted. The size of these characters is alsovarying randomly. The authors have tried to extract six different types of features from these characters to analyze the recognition accuracy and achieved maximum recognition accuracy of 91.1%.

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