Automatic Container Code Recognition via Spatial Transformer Networks and Connected Component Region Proposals

Container identification and recognition is still performed manually or in a semi-automatic fashion in multiple ports globally. This results in errors and inefficiencies in port operations. The problem of automatic container identification and recognition is challenging as the ISO standard only prescribes the pattern of the code and does not specify other parameters such as the foreground and background colors, font type and size, orientation of characters (horizontal or vertical) so on. Additionally, the corrugated surface of container body makes the two dimensional projection of the text on three dimensional containers slanted and jagged. We propose a solution in the form of an end-to-end pipeline that uses Region Proposals generated based on Connected Components for text detection in conjunction with Spatial Transformer Networks for text recognition. We demonstrate via our experimental results that the pipeline is reliable and robust even in situations when the code characters are highly distorted and outperforms the state-of-the-art results for text detection and recognition over the containers. We achieve text coverage rate of 100% and text recognition rate of 99.64%.