Handwritten word spotting based on a hybrid optimal distance
In this paper, we develop a comprehensive representation model for handwriting, which contains both morphological and topological information. An adapted Shape Context descriptor built on structural points is employed to describe the contour of the text. Graphs are first constructed by using the structural points as nodes and the skeleton of the strokes as edges. Based on graphs, Topological Node Features (TNFs) of n-neighbourhood are extracted. Bag-of-Words representation model based on the TNFs is employed to depict the topological characteristics of word images. Moreover, a novel approach for word spotting application by using the proposed model is presented. The final distance is a weighted mixture of the SC cost, and the TNF distribution comparison. Linear Discriminant Analysis (LDA) is used to learn the optimal weight for each part of the distance with the consideration of writing styles. The evaluation of the proposed approach shows the significance of combining the properties of the handwriting from different aspects.