Robust Symbol Localization Based on Junction Features and Efficient Geometry Consistency Checking

Abstract

This paper presents a new approach for symbol localization in line-drawing images using junction features and geometry consistency checking. The proposed system first detects junction points, and then characterizes them by very compact, distinctive, and varying-length descriptors. The detected junctions are used to decompose a document image into a set of smooth primitives composing of isolated shapes (e.g., isolated circles and straight lines) and curve segments bounded between either two junctions or a junction and an end-point. These primitives are then associated with a new set of keypoints to form a complete and compact representation of a graphical document. Next, keypoint matching is performed to find the correspondences among the keypoints of the query and those of database documents. The obtained matches are finally refined by a new and efficient algorithm to deal with the problem of geometry consistency checking. Our experiments showed that the proposed system is very time-and memory-efficient, and provides high accuracy rate of symbol localization.