AI Project

ABSTRACT

Detecting and recognizing text in a natural set- ting is a challenging task due to various shapes and unpredictable environment these texts are a part of. Most methods have successfully detected text instances in an environment despite the envi- ronment’s complexity. However, most methods assume the text instances are somewhat straight or in a linear format. However, it is quite common to find text that is “curved” or non-linear in the real world. Therefore, applying a text recogni- tion model on the output of a text detection model using STN fails to achieve desired results. Fur- thermore, even after detecting the text, one must rectify the text before feeding it into the text recog- nition model. Therefore, I propose a method that combines a previous text detection model((Long et al., 2018)) with a text rectification algorithm: Thin Plate Spline (TPS) and a text recognition model((Bartz et al., 2018)) to identify curved text in natural settings.

Paper can be found here

Jupyter Notebook

These are the notebooks that contains details about the TPS algorithm and how it was implemented and all the preprocessing that was done for the dataset.

Video Explanation

Here is a video that explains this project at a higher level