Supreetha Patel T P

@kittiptur.ac.in

Assistant Professor Department of CSE
Kalpataru Institute of Technology

Supreetha Patel T P


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https://researchid.co/supreethapateltp

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Computer Vision and Pattern Recognition, Engineering, Artificial Intelligence
6

Scopus Publications

22

Scholar Citations

2

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Hybrid lion optimization algorithm and dolphin echolocation-derived weighted average score-based deep handwritten character recognition framework of Kannada scripts
    Supreetha Patel Tiptur Parashivamurthy, Sannangi Viswaradhya Rajashekararadhya
    Australian Journal of Electrical and Electronics Engineering, 2025
    In Karnataka, Kannada is a commonly spoken official state language. The handwritten historical Kannada characters are preserved in the archaeological department of the manuscript preservation center, but these documents degrade the nature of the document and are also complex to read and understand the content presented in the document. So, it is highly needed for digitalizing the handwritten Kannada character files and identifying their originality. Pattern recognition in the handwritten Kannada character recognition model is a challenging research work based on its complex structure and large-scale vocabulary. The images are acquired from the standard benchmark dataset in the initial phase. Then, the images are given to the ensemble model for character recognition to improve reliability and robustness. Here, the ensemble model Weighted Averaging Score-based Ensemble Network (WAS-EnsembleNet) is designed by incorporating the Multi-scale Deep Convolutional Neural Network (M-DCNN), MobileNet, and Residual Attention Network (RAN), where the parameters in these models are optimized with the help of Hybrid algorithm Dolphin Echolocation-based Lion Optimization Algorithm (DE-LOA). Then, the Weighted Average score-based Classification is performed by getting the predictive score from these techniques. Finally, classified outcomes are attained. The efficacy of the recommended Kannada handwritten recognition model is compared by diverse performance measures.
  • HDLNet: design and development of hybrid deep learning network for optimally recognising the handwritten Kannada characters
    Supreetha Patel Tiptur Parashivamurthy, Sannangi Viswaradhya Rajashekararadhya
    Australian Journal of Electrical and Electronics Engineering, 2024
    At first, the Kannada character images are collected via benchmark datasets. After image collection, it is undergone the feature extraction process. Here, the extraction techniques are employed to acquire geometric features, texture features, and morphological features. Further, it is fused together with an optimal selection of features with optimal weights, thus it is provided as weighted fused attributes. Here, the optimisation of weight is done by the developed Fish-based Position of Marine Predators and Forest Optimisation (FP-MPFO). At last, the features which are weighted are given to a Hybrid Deep Learning Network (HDLNet), where the two models like Dense Long-Short Memory (DLSTM) and Attention-Based Deep Temporal Convolution Network (ADTCN) are incorporated with each other. To acquire the optimal value, several parameters are optimally tuned by developed FP-MPFO. Hence, the key outcomes illustrate that it has the potential to recognise the Kannada characters effectively.
  • An Efficient Kannada Handwritten Character Recognition Framework with Serial Dilated Cascade Network for Kannada Scripts
    Supreetha Patel Tiptur Parashivamurthy, Dr. Sannangi Viswaradhya Rajashekararadhya
    Advances in Artificial Intelligence and Machine Learning, 2024
    The most significant problem present in the digitized world is handwritten character recognition and identification because it is helpful in various applications. The manual work needed for changing the handwritten character document into machine-readable texts is highly reduced by using the automatic identification approaches. Due to the factors of high variance in the writing styles beyond the globe, handwritten text size and low quality of handwritten text rather than printed text make handwritten character recognition to be very complex. The Kannada language has originated over the past 1000 years, where the consonants and vowels are symmetric in nature and also curvy, therefore, the recognition of Kannada characters online is very difficult. Thus, it is essential to overcome the above-mentioned complications presented in the classical Kannada handwritten character recognition model. The recognition of characters from Kannada Scripts is also difficult. Hence, this work aims to design a new Kannada handwritten character recognition framework using deep learning techniques from Kannada scripts. There are two steps to be followed in the proposed model that is collection of images and classification of handwritten characters. At first, essential handwritten Kannada characters are collected from the benchmark resources. Next, the acquired handwritten Kannada images are offered to the handwritten Kannada character recognition phase. Here, Kannada character recognition is performed using Serial Dilated Cascade Network (SDCN), which utilized the Visual Geometry Group 16 (VGG16) and Deep Temporal Convolution Network (DTCN) technique for the observation. When compared to the baseline recognition works, the proposed handwritten Kannada character recognition model achieves a significantly higher performance rate.
  • Recognition of Kannada Character Scripts Using Hybrid Feature Extraction and Ensemble Learning Approaches
    Supreetha Patel Tiptur Parashivamurthy, Sannangi Viswaradhya Rajashekararadhya
    Cybernetics and Systems, 2024
    An automated handwritten script identification system seeks more attention in the academic research field and commercial applications. Recognizing the handwritten Kannada scripts in recent years is an active research area. But, it is a much more challenging one in the pattern recognition field owing to the complexity of structural hierarchy, huge vocabulary count, and distinct people’s diverse handwriting styles. Therefore, this paper aims to develop Kannada handwritten script recognition framework by ensemble methods. Initially, the essential Kannada handwritten text images are collected. These collected images are used for pre-processing with the CLAHE and filtering techniques and followed by segmentation with the active contour approach. The segmented images are utilized using the Adaptive Local Tetra Pattern with freeman chain code histogram techniques. Here, the parameters in the LTrP are tuned with Hybrid Honey Badger Henry Gas Solubility Optimization. Finally, the handwritten recognition is performed by Hybrid Feature Extraction with Ensemble Deep Learning (HFE-EDH). Further, the recognition rate is elevated with the parameter optimization in deep learning approaches using the HGSO + HBA algorithm. Throughout the result analysis, the accuracy of the designed method attains 96%. Thus, the proposed method reveals better performance regarding various performance measures.
  • Intelligent Character Recognition Framework for Kannada Scripts via Long Short Term Memory with Thresholding-based Segmentation
    Advances in Artificial Intelligence and Machine Learning, 2024
  • Handwritten Character Recognition of Kannada Scripts using Novel Feature Extraction Techniques and BMCNN Classifier
    Supreetha Patel Tiptur Parashivamurthy, Sannangi Viswaradhya Rajashekararadhya
    Ssrg International Journal of Electrical and Electronics Engineering, 2023
    - Handwritten Character Recognition (HCR) is one of the most popular research in recent years. Many HCR systems were developed based on various languages. However, only a few works are based on handwritten Kannada characters. Recognising handwritten Kannada characters is challenging because of the curvy and symmetric nature of the Kannada characters. Although various works were conducted for Kannada HCR, some issues must be solved. Hence, this work proposed BMCNN-based Kannada HCR. In the preprocessing phase, 3M filtering and the CLAHE techniques perform noise reduction and contrast enhancement. Then, the image is resized, angle rotated and mirror-inverted to obtain better accuracy of the input image. Then, the zonal, pattern and gradient features are extracted from the preprocessed image. Next, the significant features are selected by ISSA and then given to the BMCNN classifier to recognise the input Kannada character. To prove the efficiency of the proposed framework, the experimental analysis is conducted in terms of various measures and compared with state-of-art techniques. The results showed that the proposed recognition technique performs better than the existing techniques.

RECENT SCHOLAR PUBLICATIONS

  • Hybrid lion optimization algorithm and dolphin echolocation-derived weighted average score-based deep handwritten character recognition framework of Kannada scripts
    SPT Parashivamurthy, SV Rajashekararadhya
    Australian Journal of Electrical and Electronics Engineering 22 (3), 379-396 , 2025
    2025
    Citations: 2
  • Recognition of Kannada character scripts using hybrid feature extraction and ensemble learning approaches
    SPT Parashivamurthy, SV Rajashekararadhya
    Cybernetics and Systems 55 (8), 1977-2012 , 2024
    2024
    Citations: 14
  • HDLNet: design and development of hybrid deep learning network for optimally recognising the handwritten Kannada characters
    SPT Parashivamurthy, SV Rajashekararadhya
    Australian Journal of Electrical and Electronics Engineering 21 (3), 268-288 , 2024
    2024
    Citations: 2
  • Intelligent Character Recognition Framework for Kannada Scripts via Long Short Term Memory with Thresholding-based Segmentation.
    SPT Parashivamurthy, SV Rajashekararadhya
    Adv. Artif. Intell. Mach. Learn. 4 (3), 2517-2534 , 2024
    2024
    Citations: 2
  • An Efficient Kannada Handwritten Character Recognition Framework with Serial Dilated Cascade Network for Kannada Scripts.
    SPT Parashivamurthy, SV Rajashekararadhya
    Adv. Artif. Intell. Mach. Learn. 4 (3), 2499-2516 , 2024
    2024
    Citations: 2
  • Handwritten Character Recognition of Kannada Scripts using Novel Feature Extraction Techniques and BMCNN Classifier
    SVR Supreetha Patel Tiptur Parashivamurthy
    International Journal of Electrical and Electronics Engineering 10 (7), 125-139 , 2023
    2023
  • Deactivation of Reactive Jammers in Wireless Sensor Networks.
    SP TP, R Pallavi, P Nandini
    International Journal of Advanced Research in Computer Science 8 (3) , 2017
    2017
  • AN ENERGY EFFICIENT DEACTIVATION TECHNIQUE FOR REACTIVE JAMMERS IN WIRELESS SENSOR NETWORKS
    SP TP, KN SHREENATH
    2013

MOST CITED SCHOLAR PUBLICATIONS

  • Recognition of Kannada character scripts using hybrid feature extraction and ensemble learning approaches
    SPT Parashivamurthy, SV Rajashekararadhya
    Cybernetics and Systems 55 (8), 1977-2012 , 2024
    2024
    Citations: 14
  • Hybrid lion optimization algorithm and dolphin echolocation-derived weighted average score-based deep handwritten character recognition framework of Kannada scripts
    SPT Parashivamurthy, SV Rajashekararadhya
    Australian Journal of Electrical and Electronics Engineering 22 (3), 379-396 , 2025
    2025
    Citations: 2
  • HDLNet: design and development of hybrid deep learning network for optimally recognising the handwritten Kannada characters
    SPT Parashivamurthy, SV Rajashekararadhya
    Australian Journal of Electrical and Electronics Engineering 21 (3), 268-288 , 2024
    2024
    Citations: 2
  • Intelligent Character Recognition Framework for Kannada Scripts via Long Short Term Memory with Thresholding-based Segmentation.
    SPT Parashivamurthy, SV Rajashekararadhya
    Adv. Artif. Intell. Mach. Learn. 4 (3), 2517-2534 , 2024
    2024
    Citations: 2
  • An Efficient Kannada Handwritten Character Recognition Framework with Serial Dilated Cascade Network for Kannada Scripts.
    SPT Parashivamurthy, SV Rajashekararadhya
    Adv. Artif. Intell. Mach. Learn. 4 (3), 2499-2516 , 2024
    2024
    Citations: 2
  • Handwritten Character Recognition of Kannada Scripts using Novel Feature Extraction Techniques and BMCNN Classifier
    SVR Supreetha Patel Tiptur Parashivamurthy
    International Journal of Electrical and Electronics Engineering 10 (7), 125-139 , 2023
    2023
  • Deactivation of Reactive Jammers in Wireless Sensor Networks.
    SP TP, R Pallavi, P Nandini
    International Journal of Advanced Research in Computer Science 8 (3) , 2017
    2017
  • AN ENERGY EFFICIENT DEACTIVATION TECHNIQUE FOR REACTIVE JAMMERS IN WIRELESS SENSOR NETWORKS
    SP TP, KN SHREENATH
    2013