Enhanced Textual Image Classification with Ensemble Learning of MobileNetV2 and ResNet50

Textual image classification is crucial in various applications, such as document digitization and automatic language identification. Although ensemble learning has been increasingly utilized to improve the accuracy of deep learning models, the optimal approaches for this task are still under-explored. This research evaluates the effectiveness of ensemble learning techniques for textual image classification, focusing on combining Convolutional Neural Networks (CNNs) like MobileNetV2 and ResNet50. The dataset used in this study includes images in multiple languages, adding complexity to the classification task. By leveraging ensemble learning, we enhanced the performance of these models, achieving an accuracy of 77%, surpassing the individual CNN models. These findings suggest that ensemble learning can significantly boost the effectiveness of deep learning models in textual image classification tasks.