A Hybrid Network for Scanned Floor Plan Image Recognition and Room Labeling

Graphic recognition in scanned floor plan images is crucial to understanding indoor scene layouts and their properties. Digitization of floor plans by scanning often introduces visible and abrupt disturbances. Recognizing symbols under such a scenario with higher accuracy has always been a subject of importance in the document image analysis community. Classical machine learning methods with hand-crafted features and deep neural networks were able to achieve the task with reasonable efficiency. This paper proposes FloorCaps, a hybrid network of the recently proposed Capsule network and VGG19. The proposed network takes advantage of the VGG19 network to learn the feature encoding from region images in a floor plan and uses the Capsule network for the classification or decoding task. FloorCaps can recognize and classify the region elements present and perform room labeling in a scanned floor plan image in an end-to-end manner with much higher accuracy than the state-of-the-art methods.