Marathi Handwritten Character Recognition Using SVM and KNN Classifier

Chikmurge, Diptee; Raghunathan, Shriram

doi:10.1007/978-3-030-49336-3_32

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…It can be observed in Table 4 that the model achieved the highest accuracy of 87% and a relatively high F1 value with a test set proportion of 20%. In addition, in order to verify the reliability of the GCN model, this study selected some traditional machine learning classifers: SVM [38] and GMM [39], statistical models: HAN [40], and the graph-based methods: Node2Vec [41] and GraphSAGE [42], as references to contrast with GCN method. In Tables 7 and 8, the emergency medical supplies allocation comparison performances are shown in the form of accuracy rate and F1 value.…”

Section: Gcn Model Training Resultsmentioning

confidence: 99%

Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path

Zhao,

Peng,

Zheng

et al. 2023

Complexity

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People in the epicenter suffer from emergency medical supplies shortage in the early stage of a public health emergency because of imbalanced supply-demand in different regions or areas, which is a key issue in a major infectious disease. In response to the severe insufficiency of supplies in the epicenter, this study proposed a strategy of distributing supplies from peripheral areas to the epicenter and gave a supply-side selection model considering the epidemic influence and supplies condition in the candidate supply-side areas. First of all, the epidemic spatial-temporal transmission path (STTP) network describing the geographic spread of disease is obtained using a first-order conditional dependence approximation algorithm in a dynamic Bayesian network (DBN). Then, the structural information of the STTP network and the supplies condition characteristic information are combined using the Bipartite network embedding (BiNE) method. Finally, a graph convolutional neural network (GCN) is conducted to select the supply-side areas for peripheral-epicenter supplies distribution based on information achieved from the bipartite graph. The results show that the highest supplies allocation accuracy reaches 87%. Validation and supremacy of the proposed methodology are provided by applying it to the case in Hubei province. This study considers crossed-areas supplies distribution strategy and contributes to select suitable supply-side areas considering the epidemic and supplies condition in the peripheral areas, which is helpful to both epicenter and peripheral areas.

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Section: Gcn Model Training Resultsmentioning

confidence: 99%

Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path

Zhao,

Peng,

Zheng

et al. 2023

Complexity

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show abstract

“…The SVM and KNN with zoning features of Devanagari compound character [3] for recognition with approximately 96% accuracy. Marathi handwritten digits and characters' features were extracted using the HOG method [4] and classified using SVM and KNN with efficient performance. The score of recognition of handwritten Gurumukhi characters and numerals [5] is 87.9% for 13,000 test samples using the random forest algorithm.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Marathi Handwritten Character Recognition Using Ensemble Learning

Chikmurge¹,

Raghunathan²

2023

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In the current era, the Optical Character Recognition (OCR) model plays a vital role in converting images of handwritten characters or words into text editable script. During the COVID-19 pandemic, students' performance is assessed based on multiple-choice questions and handwritten answers so, in this situation, the need for handwritten recognition has become acute. Handwritten answers in any regional language need the OCR model to transform the readable machine-encoded text for automatic assessment which will reduce the burden of manual assessment. The single Convolutional Neural Network (CNN) algorithm recognizes the handwritten characters but its accuracy is suppressed when dataset volume is increased. In proposed work stacking and soft voting ensemble mechanisms that address multiple CNN models to recognize the handwritten characters. The performance of the ensemble mechanism is significantly better than the single CNN model. This proposed work ensemble VGG16, Alexnet and LeNet-5 as base classifiers using stacking and soft voting ensemble approaches. The overall accuracy of the proposed work is 98.66% when the soft voting ensemble has three CNN classifiers.

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Improved KNN algorithms of spherical regions based on clustering and region division

Wang

Zhao

2022

Alexandria Engineering Journal

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Marathi Handwritten Character Recognition Using SVM and KNN Classifier

Cited by 4 publications

References 13 publications

Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path

Emergency Medical Resources Allocation of Periphery for Epidemic Areas: Based on Infectious Diseases Spatial-Temporal Transmission Path

Enhancing Marathi Handwritten Character Recognition Using Ensemble Learning

Improved KNN algorithms of spherical regions based on clustering and region division

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