SketchFormer: transformer-based approach for sketch recognition using vector images

Sketches have been employed since the ancient era of cave paintings for simple illustrations to represent real-world entities and communication. The abstract nature and varied artistic styling make automatic recognition of these drawings more challenging than other areas of image classification. Moreover, the representation of sketches as a sequence of strokes instead of raster images introduces them at the correct abstract level. However, dealing with images as a sequence of small information makes it challenging. In this paper, we propose a Transformer-based network, dubbed as AttentiveNet, for sketch recognition. This architecture incorporates ordinal information to perform the classification task in real-time through vector images. We employ the proposed model to isolate the discriminating strokes of each doodle using the attention mechanism of Transformers and perform an in-depth qualitative analysis of the isolated strokes for classification of the sketch. Experimental evaluation validates that the proposed network performs favorably against state-of-the-art techniques.