Reliable and Efficient Image Processing and Deep Machine Learning for large-scale Digital Image Retrieval

Retrieving relevant photos from a database by analysing their differences and similarities is an essential part of machine vision. Numerous applications exist in areas such as storage, object recognition, localisation, and recognition with limited data. In this study, we take a look at the issue of image retrieval, in which a group of pictures is retrieved from a huge database in order to find ones that are most like the one the user has submitted (the “query image”). This thesis investigates the origins of the issue, as well as its present-day manifestations and potential solutions. To the best of our knowledge, however, there is currently no accessible generic text removal method that can be used to any font, script, language, or shape to remove all or user-specified text sections. The difficulties of multi-lingual and curved text identification and inpainting are carried over into the development of a general text eraser for real-world settings. To the best of our knowledge, however, there is currently no accessible generic text removal method that can be used to any font, script, language, or shape to remove all or user-specified text sections. Creating a general text eraser for use in real-world scenarios is difficult since it takes on the difficulties of both multi-lingual text detection and curved text inpainting. By adhering to these standards, our method significantly outperforms the state of the art on four benchmark datasets. This includes more complicated algorithms with auxiliary components. Furthermore, we offer a qualitative evaluation of our trained representation, which shows that despite its small size, it is able to gather information from highly localised and discriminative regions, much like an implicit attention mechanism.