scholarly journals AN ALGORITHM TO ANALYZE STRENGTH OF CAPTCHA

2012 ◽  
pp. 140-145
Author(s):  
AJINKYA KASHINATH PARBHANE ◽  
ANAJALI .A. CHANDAVALE ◽  
A.M. SAPKAL
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Character Recognition ◽  
Experimental Result ◽  
The Internet ◽  
Image Processing Techniques ◽  
Artificial Neural ◽  
Processing Techniques

CAPTCHA stands for Completely Automated Public Turing Tests to Tell Computers and Humans Apart. The CAPTCHAs have been widely used across the Internet to defend against undesirable and malicious bot programs. It was observed that an alarming number of CAPTCHAs could be broken by the technique of Image Processing and Artificial Neural Network. Many Researchers have tried to break a CAPTCHA so as to design robust CAPTCHA , but it is essential to generate a strong CAPTCHA that will resist bot attack. This paper has proposed algorithm to analyze the strength of CAPTCHAs using simple image processing techniques such as Preprocessing, Segmentation and Character recognition which in turn helps to improve the robustness and usability of CAPTCHA in Internet System. The experimental result shows the proposed algorithm gives 75 % accuracy to analyze the strength of CAPTCHA.

scholarly journals Screening of Tuberculosis Using Artificial Neural Network

2020 ◽  
Vol 3 (11) ◽  
pp. 145-149
Author(s):  
A. Anand Kumar ◽  
T. Mani ◽  
S. Gokulnath ◽  
S. K. Kabilesh ◽  
K. Dinakaran ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Bacterial Disease ◽  
Image Processing Techniques ◽  
Neural Organization ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Processing Techniques

Tuberculosis is an infectious bacterial disease that most commonly affects the lungs. This paper reviews, screening of tuberculosis in chest radiograph images using an artificial neural network (ANN). Implementing image processing techniques having segmentation, feature extraction from chest radiographs, at that point building up a fake neural organization for programmed characterization dependent on back proliferation calculation to group tuberculosis accurately. The performance was evaluated using SVM and ANN classifiers regarding exactness, review, and precision. The trial results Confirm the effectiveness of the proposed strategy that gives great Classification proficiency.

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.

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