Text/Figure Separation in Document Images Using Docstrum Descriptor and Two-Level Clustering

Abstract

We propose a novel algorithm for text/figure separation tailored for binary document images containing line drawings, block diagrams, charts, schemes and other kinds of business graphics. Most of the approaches for this task rely either on clever design of visual descriptor allowing to easily distinguish text and graphics regions or on the supervised learning using dataset of labeled text/figure regions. Such approaches often provide moderate separation accuracy when applied to document images which contain very diverse set of figure classes and lack sufficiently representative labeled training dataset. In contrast, our method is well-suited for vast variety of figure classes and capable of operating either in semi-supervised mode or unsupervised mode. We achieve this by leveraging unsupervised learning algorithms applied to Docstrum descriptors extracted from regions of interest and subsequent semi-supervised label propagation or unsupervised label inference. Another advantage of our method is its suitability for large scale data processing which is achieved through efficient kernel-approximating feature mapping applied to Docstrum descriptors and two-level clustering where fast mini-batch K-means algorithm is first applied to large scale data and only small number of resulting cluster centroids is subsequently processed by one of the more sophisticated clustering algorithms.