Training deep network models for accurate recognition of texts in scenes - Chen, Cheng (Nanyang Technological University, 2021)
Scene Text Recognition is an important task because of its many potential applications in the industries. However, Scene Text Recognition is also a challenging task in Computer Vision because of the irregularity and diversity of scene text images. Among these difficulties, low-resolution images are one of the major problems still yet to be perfectly solved. In this paper, a deep learning neural network specialised in scene text recognition is studied and implemented. Multiple ways to improvement model performance on low-resolution images are also investigated and compared. More specifically, two different strategies of handling low-resolution images are investigated: 1) Super-resolving images from feature level by incorporating a Super- Resolution Unit in the end-to-end trainable model; 2) Super-resolve images from image level through three different state-of-art super-resolution models. To ensure fair comparison, the TextZoom dataset is used throughout different rounds of experiments as it contains real-life low-resolution and high-resolution image pairs.