scholarly journals Images Classification of Gurumukhi Month’s Name Images using Various Convolutional Neural Network Optimizers

2021 ◽  
Author(s):  
Tajinder Pal Singh ◽  
Sheifali Gupta ◽  
Meenu Garg ◽  
Deepika Koundal ◽  
Atef Zaguia
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Complex Structure ◽  
Holistic Approach ◽  
Neural Model ◽  
Text Recognition ◽  
Computational Time ◽  
Dense Layer ◽  
Automatic Feature Extraction

Abstract The Gurumukhi script has a complex structure for which text recognition based on an analytical approach can misinterpret the script. For error-free results in text recognition, the author has proposed a holistic approach based on classification of Gurumukhi month’s name images. For this, a new convolutional neural model has been developed for automatic feature extraction from Gurumukhi text images. The proposed convolutional neural network is designed with five convolutional, three polling layers, one flatten layer and one dense layer. To validate the results of the proposed model, the dataset was self-created from 500 distinct writers. The performance of the model has been analyzed with 100 epochs, 40 batch sizes and different optimizers. The various optimizers that have been used for this experimentation are SGD, Adagrad, Adadelta, RMSprop, Adam, and Nadam. The experimental results show that the proposed CNN model performed best with Adam optimizer in terms of accuracy, computational time, F1 score, precision and recall.

RECOGNITION OF THE TEXT BY MEANS OF DEEP LEARNING

2020 ◽  
Vol 69 (1) ◽  
pp. 378-383
Author(s):  
T.A. Nurmukhanov ◽  
◽  
B.S. Daribayev ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Input Data ◽  
Optimization Methods ◽  
Text Recognition ◽  
Activation Functions

Using neural networks, various variations of the classification of objects can be performed. Neural networks are used in many areas of recognition. A big area in this area is text recognition. The paper considers the optimal way to build a network for text recognition, the use of optimal methods for activation functions, and optimizers. Also, the article checked the correctness of text recognition with different optimization methods. This article is devoted to the analysis of convolutional neural networks. In the article, a convolutional neural network model will be trained with a teacher. Teaching with a teacher is a type of training for neural networks in which you provide the input data and the desired result, that is, the student looking at the input data will understand that you need to strive for the result that was provided to him.

Plant Leaf Classification using Convolutional Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Nidhi ◽  
Jay Kant Pratap Singh Yadav
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Data ◽  
Learning Ability ◽  
Training Time ◽  
Automatic Feature Extraction ◽  
Computer Vision Applications ◽  
Shape Characteristics ◽  
And Training

Introduction: Convolutional Neural Network (CNNet) has proven the indispensable system in order to perform the recognition and classification tasks in different computer vision applications. The purpose of this study is to exploit the marvelous learning ability of CNNet in the image classification field. Method: In order to circumvent the overfitting issues and to enhance the generalization potential of the proposed FLCNNet, augmentation has been performed on the Flavia dataset that impose translation and rotation techniques to perform the augmentation with the transformed leaves having the same labels as the original ones. Both the classification models executed using; one without augmentation and one with the augmentation data are compared to check the effectiveness of the augmentation hence the aim of the proposed work. Moreover, Edge detection technique has been applied to extract the shape of the leaf images, in order to classify them accordingly. Thereafter, the FLCNNet is trained and tested for the dataset, with and without augmentation. Results: The results are gathered in terms of accuracy and training time for both datasets. The Augmented dataset (dataset 2) has been found effective and more feasible for classification without misguiding the network to learn (avoid overfitting) as compared to the dataset without augmentation (dataset 1). Conclusion: This paper proposed the Five Layer Convolution Neural Network (FLCNNet) method to classify plant leaves based on their shape. This approach can classify 8 types of leaves using automatic feature extraction, by utilizing their shape characteristics. To avoid the overfitting condition and make the performance better. We aimed to perform the classification of the augmented leaf dataset. Discussion: We proposed a five Layer CNNet (FLCNNet) to classify the leaf image data into different classes or labels based on the shape characteristics of the leaves.

LENS CLASSIFICATION ACCORDING TO THE TYPE OF LIGHT SPOT USING A NEURAL NETWORK

2020 ◽  
Vol 2020 (4) ◽  
pp. 4-14
Author(s):  
Vladimir Budak ◽  
Ekaterina Ilyina
Keyword(s):  
Neural Network ◽  
Light Intensity ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Beam Angle ◽  
New Model ◽  
Technical Parameters ◽  
Transfer Training ◽  
Trained Network

The article proposes the classification of lenses with different symmetrical beam angles and offers a scale as a spot-light’s palette. A collection of spotlight’s images was created and classified according to the proposed scale. The analysis of 788 pcs of existing lenses and reflectors with different LEDs and COBs carried out, and the dependence of the axial light intensity from beam angle was obtained. A transfer training of new deep convolutional neural network (CNN) based on the pre-trained GoogleNet was performed using this collection. GradCAM analysis showed that the trained network correctly identifies the features of objects. This work allows us to classify arbitrary spotlights with an accuracy of about 80 %. Thus, light designer can determine the class of spotlight and corresponding type of lens with its technical parameters using this new model based on CCN.

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

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