Performance analysis of Handwritten Devnagari Character Recognition using Feed Forward , Radial Basis , Elman Back Propagation, and Pattern Recognition Neural Network Model Using Different Feature Extraction Methods

This paper describes the performance analysis for the four types of neural network with different feature extraction methods for character recognition of hand written devnagari alphabets. We have implemented four types of networks i.e. Feed forward , Radial basis, Elman back propagation and Pattern recognition neural network using three different types of feature extraction methods i.e. pixel value, histogram and blocks mean for each network. These algorithms have been performed better than the conventional approaches of neural network for pattern recognition. It has been analyzed that the Radial Basis neural network performs better compared to other types of networks

Performance Evaluation of RBF, Cascade, Elman, Feed Forward and Pattern Recognition Network for Marathi Character Recognition with CLAHE Feature Extraction Method

The purpose of this paper is to study, analyze and improve the performance of RBF, Cascade, Elman, Feed Forward and Pattern Recognition Networks using �Contrast-limited Adaptive Histogram Equalization method� of featureextraction. This work is divided in to two sections. In the earlier work, we have performed the performance analysis of RBF neural network, Cascade Neural network, Elman Neural network and Feed forward neural network for the character recognition of handwritten Marathi curve scripts using �Edge detection and Dilation method� of feature extraction. In this paper, we have applied the feature extraction methodknown as Contrast-limited Adaptive Histogram Equalization (CLAHE). This feature extraction method enhances the contrast of images by transforming the values in the intensity image. For this experiment, we have considered the six samples each of 48 Marathi characters. For every sampled character, the CLAHE feature extraction method is applied. Then we have studied and analyzed the performance of these five Neural Networks for character recognition. It is found that except Elman Network, the performance of rest of all the networks is increased

Handwritten Digit Recognition and Classification Using Machine Learning

In this paper, multiple learning techniques based on Optical character recognition (OCR) for the handwritten digit recognition are examined, and a new accuracy level for recognition of the MNIST dataset is reported. The proposed framework involves three primary parts, image pre-processing, feature extraction and classification. This study strives to improve the recognition accuracy by more than 99% in handwritten digit recognition. As will be seen, pre-processing and feature extraction play crucial roles in this experiment to reach the highest accuracy

Urdu Poetry Generated by Using Deep Learning Techniques

This study provides Urdu poetry generated using different deep-learning techniques and algorithms. The data was collected through the Rekhta website, containing 1341 text files with several couplets. The data on poetry was not from any specific genre or poet. Instead, it was a collection of mixed Urdu poems and Ghazals. Different deep learning techniques, such as the model applied Long Short-term Memory Networks (LSTM) and Gated Recurrent Unit (GRU), have been used. Natural Language Processing (NLP) may be used in machine learning to understand, analyze, and generate a language humans may use and understand. Much work has been done on generating poetry for different languages using different techniques. The collection and use of data were also different for different researchers. The primary purpose of this project is to provide a model that generates Urdu poems by using data completely, not by sampling data. Also, this may generate poems in pure Urdu, not Roman Urdu, as in the base paper. The results have shown good accuracy in the poems generated by the model.Comment: 11 pages, 2 figure