scholarly journals Handwritten Bangla Numerical Digit Recognition Using Fine Regulated Deep Neural Network

2021 ◽  
Vol 9 (2) ◽  
pp. 73-84
Author(s):  
Md. Shahadat Hossain ◽  
Md. Anwar Hossain ◽  
AFM Zainul Abadin ◽  
Md. Manik Ahmed
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Network Performance ◽  
Deep Neural Network ◽  
Recognition Accuracy ◽  
Digit Recognition ◽  
Optical Character ◽  
Regularization Parameters ◽  
Handwritten Recognition

The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.

An Useful Review on Optical Character Recognition for Smart Era Generation

2021 ◽  
pp. 1-41
Author(s):  
Abhishek Das ◽  
Mihir Narayan Mohanty
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Deep Neural Network ◽  
Machine Learning Method ◽  
Learning Method ◽  
Result Section ◽  
Optical Character

In this chapter, the authors have reviewed on optical character recognition. The study belongs to both typed characters and handwritten character recognition. Online and offline character recognition are two modes of data acquisition in the field of OCR and are also studied. As deep learning is the emerging machine learning method in the field of image processing, the authors have described the method and its application of earlier works. From the study of the recurrent neural network (RNN), a special class of deep neural network is proposed for the recognition purpose. Further, convolutional neural network (CNN) is combined with RNN to check its performance. For this piece of work, Odia numerals and characters are taken as input and well recognized. The efficacy of the proposed method is explained in the result section.

Use of Deep Neural Network for Optical Character Recognition

2020 ◽  
pp. 219-254
Author(s):  
Abhishek Das ◽  
Mihir Narayan Mohanty
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Comparative Study ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Deep Neural Network ◽  
Detailed Review ◽  
Image Processing Techniques ◽  
Optical Character ◽  
Processing Techniques

In this chapter, the authors have given a detailed review on optical character recognition. Various methods are used in this field with different accuracy levels. Still there are some difficulties in recognizing handwritten characters because of different writing styles of different individuals even in a particular language. A comparative study is given to understand different types of optical character recognition along with different methods used in each type. Implementation of neural network in different forms is found in most of the works. Different image processing techniques like OCR with CNN, RNN, combination of CNN and RNN, etc. are observed in recent research works.

scholarly journals Deep Neural Network Model of Hearing-Impaired Speech-in-Noise Perception

2020 ◽  
Vol 14 ◽  
Author(s):  
Stephanie Haro ◽  
Christopher J. Smalt ◽  
Gregory A. Ciccarelli ◽  
Thomas F. Quatieri
Keyword(s):  
Neural Network ◽  
Speech Perception ◽  
Background Noise ◽  
Deep Neural Network ◽  
Recognition Accuracy ◽  
Auditory Function ◽  
Human Speech ◽  
Digit Recognition ◽  
Speech In Noise ◽  
Hearing Damage

Many individuals struggle to understand speech in listening scenarios that include reverberation and background noise. An individual's ability to understand speech arises from a combination of peripheral auditory function, central auditory function, and general cognitive abilities. The interaction of these factors complicates the prescription of treatment or therapy to improve hearing function. Damage to the auditory periphery can be studied in animals; however, this method alone is not enough to understand the impact of hearing loss on speech perception. Computational auditory models bridge the gap between animal studies and human speech perception. Perturbations to the modeled auditory systems can permit mechanism-based investigations into observed human behavior. In this study, we propose a computational model that accounts for the complex interactions between different hearing damage mechanisms and simulates human speech-in-noise perception. The model performs a digit classification task as a human would, with only acoustic sound pressure as input. Thus, we can use the model's performance as a proxy for human performance. This two-stage model consists of a biophysical cochlear-nerve spike generator followed by a deep neural network (DNN) classifier. We hypothesize that sudden damage to the periphery affects speech perception and that central nervous system adaptation over time may compensate for peripheral hearing damage. Our model achieved human-like performance across signal-to-noise ratios (SNRs) under normal-hearing (NH) cochlear settings, achieving 50% digit recognition accuracy at −20.7 dB SNR. Results were comparable to eight NH participants on the same task who achieved 50% behavioral performance at −22 dB SNR. We also simulated medial olivocochlear reflex (MOCR) and auditory nerve fiber (ANF) loss, which worsened digit-recognition accuracy at lower SNRs compared to higher SNRs. Our simulated performance following ANF loss is consistent with the hypothesis that cochlear synaptopathy impacts communication in background noise more so than in quiet. Following the insult of various cochlear degradations, we implemented extreme and conservative adaptation through the DNN. At the lowest SNRs (<0 dB), both adapted models were unable to fully recover NH performance, even with hundreds of thousands of training samples. This implies a limit on performance recovery following peripheral damage in our human-inspired DNN architecture.

Convolutional Neural Network Based Intelligent Advertisement Search Framework for Online English Newspapers

2021 ◽  
Vol 15 ◽  
Author(s):  
Pooja Jain ◽  
Kavita Taneja ◽  
Harmunish Taneja
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Access To Information ◽  
Intelligent Environment ◽  
Online Newspapers ◽  
Manual Search ◽  
Optical Character ◽  
Extraction Algorithm

Background: Instant access to desired information is crucial for building an intelligent environment that creates value for people and steering towards society 5.0. Online newspapers are one such example that provides instant access to information anywhere and anytime on our mobiles, tablets, laptops, desktops, etc. However, when it comes to searching for a specific advertisement in newspapers, online newspapers do not provide easy advertisement search options. In addition, there are no specialized search portals for keyword-based advertisement searches across multiple online newspapers. As a result, to find a specific advertisement in multiple newspapers, a sequential manual search is required across a range of online newspapers. Objective: This research paper proposes a keyword-based advertisement search framework to provide an instant access to the relevant advertisements from online English newspapers in a category of reader’s choice. Method: First, an image extraction algorithm is proposed to identify and extract the images from online newspapers without using any rules on advertisement placement and size. It is followed by a proposed deep learning Convolutional Neural Network (CNN) model named ‘Adv_Recognizer’ to separate the advertisement images from non-advertisement images. Another CNN Model, ‘Adv_Classifier’, is proposed, classifying the advertisement images into four pre-defined categories. Finally, the Optical Character Recognition (OCR) technique performs keyword-based advertisement searches in various types across multiple newspapers. Results: The proposed image extraction algorithm can easily extract all types of well-bounded images from different online newspapers. This algorithm is used to create an ‘English newspaper image dataset’ of 11,000 images, including advertisements and non-advertisements. The proposed ‘Adv_Recognizer’ model separates advertising and non-advertisement pictures with an accuracy of around 97.8%. In addition, the proposed ‘Adv_Classifier’ model classifies the advertisements in four pre-defined categories exhibiting an accuracy of approximately 73.5%. Conclusion: The proposed framework will help newspaper readers perform exhaustive advertisement searches across various online English newspapers in a category of their interest. It will also help in carrying out advertisement analysis and studies.

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