A Hybrid Deep Learning Framework with Weighted Voting Ensemble of DenseNet and ResNet for watermark Text Classification

In recent years, the proliferation of digital content has led to increased concerns about intellectual property protection and content authenticity. Digital watermarks, especially those embedded as text in scene images, have emerged as a vital tool for asserting ownership and preventing unauthorized use. However, detecting and classifying such watermarked text in complex natural scenes remains a challenging task due to varying backgrounds, fonts, distortions and noise. This research focuses on the classification of watermarked and non-watermarked text in scene images. To achieve this, we used deep convolutional neural networks, DenseNet-169, Squeeze-and- Excitation(SE) -ResNet and Wide ResNet, for robust feature extraction. The extracted features were then passed to Machine Learning (ML) classifiers, including Random Forest (RF), Support Vector Ma- chine (SVM), and Logistic Regression (LR), to per- form the final classification. In addition, a weighted voting mechanism was implemented to combine the outputs of individual classifiers and improve over- all accuracy. The proposed approach demonstrates promising results are distinguishing between water- marked and non-watermarked text, contributing to the advancement of automated watermark detection systems. The proposed method shows promising results, with the highest accuracy of 97.13% achieved using SE- ResNet with weighted voting, followed by 95.08% using DenseNet201. This highlights SE-ResNet’s effectiveness in ensemble classification for watermark detection.