CONFIRM – Clustering of noisy form images using robust matching. (March 2019) - Tensmeyer, Chris; Martinez, Tony
Highlights: A clustering framework is proposed for clustering noisy form images. Novel algorithms for matching text lines and rule lines are introduced. We show 44% improvement over the state-of-the-art on 5 datasets of historical forms. Sampling and bootstrapping is employed for scalability to large datasets. Abstract: Identifying the type of a scanned form image greatly facilitates automated processing, including field segmentation and field recognition. Contrary to most prior work, we focus on unsupervised type identification, where the possible form types for a given collection are not known apriori . Our target domain is noisy collections of form images that contain structurally similar, yet objectively different, form types, which are challenging to differentiate in an unsupervised setting. This work presents a novel algorithm: CONFIRM (Clustering Of Noisy Form Images using Robust Matching), which simultaneously discovers the set of form types in a collection and assigns a type to each form. CONFIRM matches type-set text and rule lines between forms to create collection-specific features, which we show outperform the Bag of Visual Word (BoVW) approach employed by the current state-of-the-art in form image clustering. CONFIRM scales well to large document collections with a bootstrap clustering process, in which only a small subset of the data is clustered directly, and the rest of the data is assigned to clusters in linear time. We show that CONFIRM reduces cluster impurity on average by 44% compared to the state-of-the art on 5 collections of historical forms that contain structurally similar form types.