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Improving Ocr Accuracy Of Damaged Pictures With Generative Adversarial Networks, Pu Du
Improving Ocr Accuracy Of Damaged Pictures With Generative Adversarial Networks, Pu Du
LSU Master's Theses
In this thesis, we focus on resolving the inpainting problem and improving Optical Character Recognition (OCR) accuracy of damaged text images at character level. We present a Generative Adversarial Network (GAN)-based model conditioned on class labels for image inpainting. This model is a deep convolutional neural network with encoder-decoder style architecture which can process images with holes at random locations. Experiments on the character images dataset demonstrate that our proposed model generates promising inpainting results and significantly improve OCR accuracy by reconstructing missing parts of damaged character images.
Applying Deep Learning Techniques To The Analysis Of Android Apks, Robin Andrew Nix
Applying Deep Learning Techniques To The Analysis Of Android Apks, Robin Andrew Nix
LSU Master's Theses
Malware targeting mobile devices is a pervasive problem in modern life and as such tools to detect and classify malware are of great value. This paper seeks to demonstrate the effectiveness of Deep Learning Techniques, specifically Convolutional Neural Networks, in detecting and classifying malware targeting the Android operating system. Unlike many current detection techniques, which require the use of relatively rigid features to aid in detection, deep neural networks are capable of automatically learning flexible features which may be more resilient to obfuscation. We present a parsing for extracting sequences of API calls which can be used to describe a …