Machine Learning-based Classification of Indian Caste Certificates using GLCM Features

In today's world, there is a growing prevalence of fraudulent Caste certificate schemes, posing a threat to society. It is essential to prevent the issuance of these false documents as they can potentially disrupt the social order. Accurate classification and verification of Indian Caste certificates to prevent counterfeiting can ensure equitable access to benefits. Implementing such digital systems can help thwart these scams, creating a community devoid of counterfeit documents and promoting a strong social welfare framework. The detection process includes analyzing scanned copies against authentic references using image processing methods. This study tackles the issue using texture features extracted through the Gray Level Co-occurrence Matrix (GLCM) and diverse machine learning (ML) algorithms. The approach encompasses image pre-processing, GLCM feature extraction, and the application of classifiers such as K-Nearest Neighbor (KNN), Decision Tree (DT),Support Vector Machine (SVM),Naive Bayes (NB), andRandom Forest (RF). Assessments based on accuracy, confusion matrices, and AUC (area under curve) scores indicate that the Naive Bayes classifier outperforms other methods with 100%accuracy and a robust AUC score. These findings indicate that integrating GLCM features with ML algorithms offers a dependable solution for Caste certificate authentication.