Evaluation of Discriminative Models for the Reconstruction of Hand-Torn Documents

This work deals with the reconstruction of hand-torn documents from pairs of aligned fragments. In the first step we use a recent approach to estimate hypotheses for aligning pieces from a set of magazine pages. We then train a structural support vector machine to determine the compatibility of previously aligned pieces along their adjacent contour regions. Based on the output of this discriminative model we induce a ranking among all pairs of pieces, as high compatibility scores often correlate with spatial configurations found in the original document. To evaluate our system’s performance we provide a new baseline on a publicly available benchmark dataset in terms of mean average precision (mAP). With the (mean) average precision being widely recognized as de facto standard for evaluation of object detection and retrieval methods, our work is devoted to establish this performance measure for document reconstruction to enable a rigorous comparison of different methods.