Field Of Handwritten Character Recognition Research Articles

Comparing filter and wrapper approaches for feature selection in handwritten character recognition

It is generally agreed that the selection of an appropriate set of features is a fundamental process in the development of any pattern recognition system. Its purpose is to identify the truly distinctive subset of features to reduce the size of the search space, without decreasing the classification performance. This problem is particularly relevant in the field of handwriting recognition, due to the enormous variability of character shape, which has led to the development of a large variety of feature sets that are becoming increasingly larger in terms of the number of attributes. While promising, the results achieved so far have several limitations, which include, among others, the computational complexity of selecting and evaluating feature subsets and the difficulty in evaluating the interactions among features. In a previous study, we tried to overcome some of the above limitations by adopting a feature-ranking-based technique: a large study was carried out considering different filter-based techniques for feature subset evaluation. The aim of this work is to extend the previous study by presenting a broad comparison between filter and wrapper techniques for feature selection in the field of handwritten character recognition. In the experiments, we analysed one of the most effective and widely used set of features in handwriting recognition, applied to standard real-word databases of handwritten characters. The experimental results confirmed that filter and wrapper approaches achieve similar performances, with the former selecting fewer features at a lower computational cost.

Optical Character Recognition System for Digit Recognition Using Deep Learning

Because it is so difficult to distinguish handwritten digits, digit identification is one of the most critical applications in computer vision. This is one of the reasons why it is so tough. The field of handwritten character recognition is one in which a great deal of application of numerous deep learning models has occurred. The startling parallels that can be drawn between deep learning and the brain are primarily responsible for its meteoric rise in popularity. In this study, the Artificial Neural Network and the Convolutional Neural Network, two of the most used Deep Learning algorithms, were investigated with an eye toward the recognition process's feature extraction and classification phases. With the assistance of the categorical cross-entropy loss and the ADAM optimizer, the models were trained on the MNIST dataset. Backpropagation and gradient descent are the two methods utilized during the training process of neural networks that contain reLU activations and carry out automatic feature extraction. In computer vision, one of the most common and widely used classifiers is the Convolution Neural Network, sometimes referred to as ConvNets or Convolutional neural networks. This network is used for the recognition and categorization of images.

Recognition of Offline Gujarati Handwritten Disjoint Consonants using Pattern Matching

Image processing is one of the most popular field nowadays. Recognition of the offline isolated handwritten characters is an area which got lot of attention within the field of Image Processing. Various techniques have been proposed in the area of online and offline handwritten character recognition (HCR). In future HCR is the key factor for the transformation of written or printed text into system understandable format. Thus, providing a boost to digitization era. Gujarati is one such language which has many challenges in creation of an accurate OCR. Researchers have achieved good accuracy in the field of online Gujarati handwritten character recognition. This paper introduces a pattern recognition system which is able to recognize isolated offline Gujarati handwritten characters with higher accuracy. Experiments have been done on sub set of ( g, `, l, x, h ) consonants using a total data set of 6750 handwritten consonants by different individuals. The experimental results achieved a markable contribution in the field of handwritten character recognition.

Handwritten Character Recognition using Neural Network and Tensor Flow

The paper will describe the best approach to get more than 90% accuracy in the field of Handwritten Character Recognition (HCR). There have been plenty of research done in the field of HCR but still it is an open problem as we are still lacking in getting the best accuracy. In this paper, the offline handwritten character recognition will be done using Convolutional neural network and Tensorflow. A method called Soft Max Regression is used for assigning the probabilities to handwritten characters being one of the several characters as it gives the values between 0 and 1 summing up to 1. The purpose is to develop the software with a very high accuracy rate and with minimal time and space complexity and also optimal.

A Hybrid Approach for Recognition of Hand Written Devnagri Compound Characters

Being an effective tool in the world of communication, numerous techniques have been developed for documenting the handwritten text. Few of the exceptional techniques describe the environment of handwritten scripts and further convert it into electronic data by implementing various algorithms. Devnagri is one of the widely used scripts for most popular and commonly used languages like Marathi and Hindi. Recent development in the field of handwritten character recognition based on different methodologies like neural network, fuzzy logic, and deep neural networks has shown remarkable improvement in character recognition accuracy from 75% to 96%. We propose a fuzzy-Neural hybrid approach for recognition of hand written Devnagri compound character that uses a rotation invariant rule-based thinning algorithm as one of the major pre-processing activity. Thinning the characters to their central line, preserving the shape of the character are the distinctive features of thinning algorithm. Concurrent application of different rules to each pixel of the character image results into symmetrical thinning as well as improves the overall speed of the system. The system is trained using Neural Network where the weights are optimized using fuzzy rules improving the accuracy of the system.Results obtained for the fuzzy-neural based system with thinning helps in preserving the topology of the characters written in Devnagri and prove that accuracy of the system has stabilized in the band of 92-97% which was fluctuating in the band of 89-94% for the previously implemented systems. The system also shows a substantial improvement in accuracy for recognition of compound characters in comparison with our previously implemented system.

Gujrati character recognition using weighted k-NN and Mean χ 2 distance measure

With advances in the field of digitization, document analysis and handwriting recognition have emerged as key research areas. Authors present a handwritten character recognition system for Gujrati, an Indian language spoken by 40 million people. The proposed system extracts four features. A unique pattern descriptor and Gabor phase XNOR pattern are the two features that are newly proposed for isolated handwritten character set of Gujrati. In addition to these two features, we use contour direction probability distribution function and autocorrelation features. Next contribution is the weighted k-NN classifier. This research finally contributes is a novel mean χ 2 distance measure. Proposed classifier exploits a combination of feature weights, new distance measure along with a triangular distance and Euclidian distance for performance that improves conventional k-NN classifier. The implementation on a comprehensive data set show 86.33 % recognition efficiency. Facts and figures show that proposed approach outperforms conventional k-NN. It is concluded that despite the shape ambiguities in Indian scripts, proposed classification algorithm could be a dominant technique in the field of handwritten character recognition.

Adaptive Membership Functions for Handwritten Character Recognition by Voronoi-Based Image Zoning

In the field of handwritten character recognition, image zoning is a widespread technique for feature extraction since it is rightly considered to be able to cope with handwritten pattern variability. As a matter of fact, the problem of zoning design has attracted many researchers who have proposed several image-zoning topologies, according to static and dynamic strategies. Unfortunately, little attention has been paid so far to the role of feature-zone membership functions that define the way in which a feature influences different zones of the zoning method. The result is that the membership functions defined to date follow nonadaptive, global approaches that are unable to model local information on feature distributions. In this paper, a new class of zone-based membership functions with adaptive capabilities is introduced and its effectiveness is shown. The basic idea is to select, for each zone of the zoning method, the membership function best suited to exploit the characteristics of the feature distribution of that zone. In addition, a genetic algorithm is proposed to determine-in a unique process-the most favorable membership functions along with the optimal zoning topology, described by Voronoi tessellation. The experimental tests show the superiority of the new technique with respect to traditional zoning methods.