Forthcoming and Online First Articles

International Journal of Applied Pattern Recognition

International Journal of Applied Pattern Recognition (IJAPR)

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International Journal of Applied Pattern Recognition (2 papers in press)

Regular Issues

  • Automation of Noise Sampling in Deep Reinforcement Learning   Order a copy of this article
    by Kunal Karda, Namit Dubey, Abhas Kanungo, VARUN GUPTA 
    Abstract: The actor-critic models are generally prone to overestimation of sub- optimal policies and Q-values. Our proposed approach is established on value-based deep reinforcement learning algorithm also know Twin Delayed Deep Deterministic Policy Gradient algorithm or TD3. The suggested approach is used to solve complex reinforcement learning problem like Half-Humanoid robot, Ant, Half-Cheetah to cover a path. This problem can only be solved with an algorithm which can work on continuous-action spaces, without much delaying the result to propagate during the inference of model. The proposed model has been adapted to converge faster to optimal Q-Values. The TD3 uses two Deep neural networks for learning two Q-Values viz. Q1 and Q2, in the proposed approach the Q-values average is being taken as an input for final Q-Value unlike the other reinforcement learning algorithm such as DDPG which is prone to overestimate the Q-Values. The proposed approach has also made self-adjusting noise clipping function which make it harder for the policy to exploit Q-function errors to further improve performance.
    Keywords: TD3; Q-Values; Deep neural networks; Half-Humanoid robot; Ant; Half-Cheetah; Reinforcement Learning.

  • Identification of Typewritten and Handwritten Conjunct Gujarati Characters using Artificial Neural Network   Order a copy of this article
    by Bharat Patel 
    Abstract: Gujarati script has a large number of characters with curvature shapes and complexities. The script can contain a variety of character sets like vowels, consonants, numerals, modifiers, conjunct characters, and other combinations of characters. Different forms of conjunct characters are possible but in this particular paper one of the forms of frequently used conjunct characters are considered for assessment point of view. This paper deals with the identification of typewritten and handwritten conjunct characters. There is no benchmark dataset available for Conjunct Gujarati characters; so, a train and test dataset are created using various features of characters such as a number of open edges, location of open edges in zone, pixels count on a constructed horizontal and vertical line. Artificial neural network is used for the classification of characters and a success rate of 99.4% and 94.1% is achieved in most of the typewritten and handwritten Gujarati conjunct characters respectively.
    Keywords: typewritten; handwritten; conjunct Gujarati character; artificial neural network; feature extraction; optical character recognition.
    DOI: 10.1504/IJAPR.2022.10043516
     

 

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