Optical Character Recognition on Bank Cheques Using 2D Convolution Neural Network

2018 ◽  
pp. 589-596 ◽  
Author(s):  
Shriansh Srivastava ◽  
J. Priyadarshini ◽  
Sachin Gopal ◽  
Sanchay Gupta ◽  
Har Shobhit Dayal
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Convolution Neural Network ◽  
Optical Character

scholarly journals Handwritten Tamil Character Recognition Using Convolution Neural Network by Adam Optimizer

2021 ◽  
pp. 40-45
Author(s):  
Mrs. R. Iyswarya ◽  
S. Deepak ◽  
P. Jagathratchagan ◽  
Jai Kailash
Keyword(s):  
Neural Network ◽  
Performance Measures ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Convolution Neural Network ◽  
Handwritten Character Recognition ◽  
Considerable Research ◽  
Optical Character ◽  
Electronic Method ◽  
And Performance

Optical Character Recognition is a widely used electronic method for recognition of handwritten images. Tamil handwritten character recognition is complex to recognize. Hence considerable research efforts have been taken in this field. The complexities of writers and the characters, structure over looping and unwanted character portions are the major challenges faced in Tamil characters. RGB to grayscale conversion, image complementation and structure morphing are enclosed in the preprocessing phase. The processed images are subject to recognition with optimized CNN. The connected layers are enhanced using ADAM optimizer for improvement of the standard. The accuracy and performance of the proposed work is compared with other models with certain performance measures.

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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