Learning Diagram Parts with Hidden Random Fields

Many diagrams contain compound objects composed of parts. We propose a recognition framework that learns parts in an unsupervised way, and requires training labels only for compound objects. Thus, human labeling effort is reduced and parts are not predetermined, instead appropriate parts are discovered based on the data. We model contextual relations between parts, such that the label of a part can depend simultaneously on the labels of its neighbors, as well as spatial and temporal information. The model is a Hidden Random Field (HRF), an extension of a Conditional Random Field. We apply it to find parts of boxes, arrows and flowchart shapes in hand-drawn diagrams, and also demonstrate improved recognition accuracy over the conditional random field model without parts.