Grouping Strokes into Shapes in Hand-Drawn Diagrams

Abstract

Objects in freely-drawn sketches often have no spatial or temporal separation, making object recognition difficult. We present a two-step stroke-grouping algorithm that first classifies individual strokes according to the type of object to which they belong, then groups strokes with like classifications into clusters representing individual objects. The first step facilitates clustering by naturally separating the strokes, and both steps fluidly integrate spatial and temporal information. Our approach to grouping is unique in its formulation as an efficient classification task rather than, for example, an expensive search task. Our single-stroke classifier performs at least as well as existing single-stroke classifiers on text vs. nontext classification, and we present the first three-way single-stroke classification results. Our stroke grouping results are the first reported of their kind; our grouping algorithm correctly groups between 86% and 91% of the ink in diagrams from two domains, with between 69% and 79% of shapes being perfectly clustered.

A Novel Method for Recognizing Sketched Objects by Learning Their Geometrical Features

“…Although sketch recognition has been studied for more than twenty years, the focus in previous research has been mostly on producing methods that can identify geometric shapes separately. Most of the existed work was limited to some narrow domains, such as chemical drawings (Ouyang and Davis) [7], diagrams (Peterson et al) [14] , or simple hand-drawn shapes such as circles, rectangles, and triangles (Paulson and Hammond [15] ; Manuel J. Fonseca Joaquim A. Jorge [16] . Yongxin & Timothy developed the first deep neural network model (sketch image retrieval system) for sketch classification and it has outperformed state-of-the-art results in the sketch benchmark of [17].…”