Sketch Recognition

Sketch recognition focuses on automatically interpreting hand-drawn sketches, aiming to bridge the gap between human creativity and machine understanding. Current research emphasizes developing robust models, such as autoregressive networks and CycleGANs, that can handle the variability and sparsity inherent in sketches, often leveraging techniques like primitive abstraction and edge detection to improve accuracy and explainability. This field is significant for its potential to improve human-computer interaction, enabling applications in design, education, and digitalization of hand-drawn diagrams like flowcharts and mind maps. Furthermore, research is actively exploring how to train these models effectively, even with limited sketch data, by utilizing alternative sources like natural images.