Text Spotting towards Perceptually Aliased Urban Place Recognition

Abstract

Recognizing places of interest (POIs) can be challenging for humans, especially in foreign environments. In this study, we leverage smartphone sensors (i.e., camera, GPS) and deep learning algorithms to propose an intelligent solution to recognize POIs in an urban environment. Recent studies have approached landmark recognition as an image retrieval problem. However, visual similarity alone is not robust against challenging conditions such as extreme appearance variance and perceptual aliasing in urban environments. To this end, we propose to fuse visual, textual, and positioning information. Our contributions are as follows. Firstly, we propose VPR through text reading pipeline (VPRText) that uses off-the-shelf text spotting algorithms for word spotting followed by layout analysis and text similarity search modules. Secondly, we propose a hierarchical architecture that combines VPRText and image retrieval. Thirdly, we perform a comprehensive empirical study on the applicability of state-of-the-art text spotting methods for the VPR task. Additionally, we introduce a challenging purpose-built urban dataset for VPR evaluation. The proposed VPR architecture achieves a superior performance overall, especially in challenging conditions (i.e., perceptually aliased and illuminated environments).