Abstract
In the interpretation of images, contextual comprehension serves a key task in a natural scene. The text present in scene images often bestows qualitative details which can be analyzed in many aspects of image processing. The text and non-text natural image classification help in pruning out unnecessary processing to get the key textual information. In this work, we designed a convolutional neural network (CNN), named TNTNet, and also a transfer learning-based method was considered for this purpose. We proposed an ensemble network by combining the TNTNet and the pre-trained VGG-19 model. The accuracies were computed for different train-test ratios. The highest accuracy of 99.45% was obtained in our ensemble network.
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Ghosh, M., Chatterjee, S., Mukherjee, H., Sen, S., Obaidullah, S.M. (2022). Text/Non-text Scene Image Classification Using Deep Ensemble Network. In: Mandal, J.K., Buyya, R., De, D. (eds) Proceedings of International Conference on Advanced Computing Applications. Advances in Intelligent Systems and Computing, vol 1406. Springer, Singapore. https://doi.org/10.1007/978-981-16-5207-3_47
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DOI: https://doi.org/10.1007/978-981-16-5207-3_47
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