Multi Variant Handwritten Telugu Character Recognition Using Transfer Learning

Ganji, Tejasree; Velpuru, Muni Sekhar; Dugyala, Raman

doi:10.1088/1757-899x/1042/1/012026

Cited by 13 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Movva and Pulabaigari [19] proposed that geometric moment based features can be adopted for representing a stroke that possesses the required invariance properties and the utilization of NN for recognizing the equivalent characters from the stroke combination and the positional data of the strokes. Ganji et al [20] concentrated on character image recognition, classification, and segmentation. They had executed the presented module on the basis of enhanced DL methods for Telugu Handwritten Character (scanned handwritten image) detection, classification, and segmentation.…”

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Telugu Handwritten Character Recognition Using Multi-Objective Mayfly Optimization with Deep Learning–Based DenseNet Model

Sonthi

Nagarajan

Krishnaraj

2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

View full text Add to dashboard Cite

Handwritten character recognition process has gained significant attention among research communities due to the application in assistive technologies for visually impaired people, human robot interaction, automated registry for business document, and so on. Handwritten character recognition of Telugu language is hard owing to the absence of massive dataset and trained convolution neural network (CNN). Therefore, this paper introduces an intelligent Telugu character recognition using multi-objective mayfly optimization with deep learning (MOMFO-DL) model. The proposed MOMFO-DL technique involves DenseNet-169 model as a feature extractor to generate a useful set of feature vectors. Moreover, functional link neural network (FLNN) is used as a classification model to recognize and classify the printer characters. The design of MOMFO technique for the parameter optimization of DenseNet model and FLNN model shows the novelty of the work. The use of MOMFO technique helps to optimally tune the parameters in such a way that the overall performance can be improved. The extensive experimental analysis takes place on benchmark datasets and the outcomes are examined with respect to different measures. The experimental results pointed out the supremacy of the MOMFO technique over the recent state of art methods.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

An Intelligent Telugu Handwritten Character Recognition Using Multi-Objective Mayfly Optimization with Deep Learning–Based DenseNet Model

Sonthi

Nagarajan

Krishnaraj

2023

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

View full text Add to dashboard Cite

show abstract

“…The proposed model addressed the challenge of training the system with higher accuracy using fewer samples. DenseNet201 and deep transfer learning algorithms [ 96 ] for multiclass brain tumor classification. Feature selection was done using Entropy–Kurtosis-based High Feature Values (EKbHFV) and Modified Genetic Algorithm (MGA) techniques from BRATS2018 and BRATS2019 datasets [ 97 ].…”

Section: Case Studiesmentioning

confidence: 99%

A Comprehensive Review on Smart Health Care: Applications, Paradigms, and Challenges with Case Studies

Raoof

Durai

2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

Growth and advancement of the Deep Learning (DL) and the Internet of Things (IoT) are figuring out their way over the modern contemporary world through integrating various technologies in distinct fields viz, agriculture, manufacturing, energy, transportation, supply chains, cities, healthcare, and so on. Researchers had identified the feasibility of integrating deep learning, cloud, and IoT to enhance the overall automation, where IoT may prolong its application area through utilizing cloud services and the cloud can even prolong its applications through data acquired by IoT devices like sensors and deep learning for disease detection and diagnosis. This study explains a summary of various techniques utilized in smart healthcare, i.e., deep learning, cloud-based-IoT applications in smart healthcare, fog computing in smart healthcare, and challenges and issues faced by smart healthcare and it presents a wider scope as it is not intended for a particular application such aspatient monitoring, disease detection, and diagnosing and the technologies used for developing this smart systems are outlined. Smart health bestows the quality of life. Convenient and comfortable living is made possible by the services provided by smart healthcare systems (SHSs). Since healthcare is a massive area with enormous data and a broad spectrum of diseases associated with different organs, immense research can be done to overcome the drawbacks of traditional healthcare methods. Deep learning with IoT can effectively be applied in the healthcare sector to automate the diagnosing and treatment process even in rural areas remotely. Applications may include disease prevention and diagnosis, fitness and patient monitoring, food monitoring, mobile health, telemedicine, emergency systems, assisted living, self-management of chronic diseases, and so on.

show abstract

“…Lastly, with F_1, the DLTCR-PHWC algorithm has provided a precision of 99.58%, recall of 99.82%, accuracy of 99.78%, F1-measure of 98.03%, and kappa of 99.34%. Finally, a detailed comparative results analysis of the DLTCR-PHWC technique takes place in Table II [22][23][24][25][26]. Fig.…”

Section: Performance Validationmentioning

confidence: 99%

Automated Telugu Printed and Handwritten Character Recognition in Single Image using Aquila Optimizer based Deep Learning Model

Sonthi¹,

Nagarajan²,

Krishnaraj³

2021

IJACSA

View full text Add to dashboard Cite

Machine printed or handwritten character recognition becomes a major research topic in several real time applications. The recent advancements of deep learning and image processing techniques can be employed for printed and handwritten character recognition. Telugu character Recognition (TCR) remains a difficult task in optical character recognition (OCR), which transforms the printed and handwritten characters into respective text formats. In this aspect, this study introduces an effective deep learning based TCR model for printed and handwritten characters (DLTCR-PHWC). The proposed DLTCR-PHWC technique aims to detect and recognize the printed as well as handwritten characters that exist in the same image. Primarily, image pre-processing is performed using the adaptive fuzzy filtering technique. Next, line and character segmentation processes are performed to derive useful regions. In addition, the fusion of EfficientNet and CapsuleNet models is used for feature extraction. Finally, the Aquila optimizer (AO) with bi-directional long short-term memory (BiLSTM) model is utilized for recognition process. A detailed experimentation of the proposed DLTCR-PHWC technique is investigated using Telugu character dataset and the simulation outcome portrayed the supremacy of the proposed DLTCR-PHWC technique over the recent state of art approaches.

show abstract

Multi Variant Handwritten Telugu Character Recognition Using Transfer Learning

Cited by 13 publications

References 0 publications

An Intelligent Telugu Handwritten Character Recognition Using Multi-Objective Mayfly Optimization with Deep Learning–Based DenseNet Model

An Intelligent Telugu Handwritten Character Recognition Using Multi-Objective Mayfly Optimization with Deep Learning–Based DenseNet Model

A Comprehensive Review on Smart Health Care: Applications, Paradigms, and Challenges with Case Studies

Automated Telugu Printed and Handwritten Character Recognition in Single Image using Aquila Optimizer based Deep Learning Model

Contact Info

Product

Resources

About