Semester of Graduation

Spring 2020

Degree

Master of Science in Computer Science (MSCS)

Department

Division of Computer Science and Engineering

Document Type

Thesis

Abstract

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.

Committee Chair

Xin Li

Available for download on Wednesday, January 25, 2023

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