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Convolutional Neural Network and Histogram of Oriented Gradient Based Invariant Handwritten MODI Character Recognition

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Abstract—

“MODI lipi” is one of the Indian ancient scripts and is as yet unrecognized script. It is not in use today, but it has importance for historical researchers of not only the ancient Maratha history but history in different regions of India. The recognition of MODI demands transform invariant approach, as MODI documents are deformed severely. Invariant handwritten character recognition is accomplished in the past by employing feature extraction methods, yet there is a scope to improve the results under global transformations. At present, convolutional neural network exhibits only local transform invariance impulsively by convolution-pooling architecture and data augmentation. To achieve global invariance for MODI recognition, the proposed classification framework used CNN-based transfer learning and a global feature extractor histogram of oriented gradient. Additionally, the criterion based on principal component analysis and confusion matrix are introduced to choose the invariant feature and to find classes responsible for poor recognition rate. The proposed classifiers are trained on a self-created handwritten MODI character dataset and tested on transformed MODI dataset. The results showed that the proposed framework is effective to recognize MODI handwritten characters under transformations without data augmentation and network alteration.

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ACKNOWLEDGMENTS

The authors express gratitude to Dr. Vishwanath Karad MITWPU, Pune, Maharashtra, India for providing the GeForce GTX TITAN by NVIDIA for experimentations.

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Authors and Affiliations

  1. School of Electronics and Communication Engineering, Dr. Vishwanath Karad MITWPU, 411038, Kothrud, Pune, India

    Savitri Jadhav

  2. Department of Computer Engineering, Govt. College of Engineering and research, 412405, Awasari (Khurd), Pune, India

    Vandana Inamdar

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Correspondence to Savitri Jadhav or Vandana Inamdar.

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This article is a completely original work of its authors; it has not been published before and will not be sent to other publications until the PRIA Editorial Board decides not to accept it for publication.

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Savitri Nathrao Jadhav received her post graduate degree from University of Pune, Maharashtra, India, in 2009. She is currently working as an Assistant Professor in the School of Electronics and Communication Engineering, Dr. Vishawanath Karad MITWPU, Pune, India. She is an academician having a total of 14 years of experience. She is a member of SAE-India.

Vandana S. Inamdar received her Ph.D degree from Savitribai Phule Pune University, Pune India. She has worked as an Associate Professor in the Department of Computer Engineering and Information Technology, College of Engineering, Pune, India. She is currently as an Associate Professor in the Department of Computer Engineering, Govt. College of Engineering and research, Awasari (Khurd), Pune, India. She has 28 years of academic experience and around forty publications to her credit. She is a Member of IEEE Signal Processing Society, CSI India, IETE, and ISTE.

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Savitri Jadhav, Vandana Inamdar Convolutional Neural Network and Histogram of Oriented Gradient Based Invariant Handwritten MODI Character Recognition. Pattern Recognit. Image Anal. 32, 402–418 (2022). https://doi.org/10.1134/S1054661822020109

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