Marketing materials such as flyers and other infographics are a vast online resource.In a number of industries, such as the commercial real estate industry, they are in fact the only authoritative source of information.Companies attempting to organize commercial real estate inventories spend a significant amount of resources on manual data entry of this information.In this work, we propose a method for extracting structured data from free-form commercial real estate flyers in PDF and HTML formats.We modeled the problem as text categorization and Named Entity Recognition (NER) tasks and applied a supervised machine learning approach (Support Vector Machines).Our dataset consists of more than 2,200 commercial real estate flyers and associated manually entered structured data, which was used to automatically create training datasets.Traditionally, text categorization and NER approaches are based on textual information only.However, information in visually rich formats such as PDF and HTML is often conveyed by a combination of textual and visual features.Large fonts, visually salient colors, and positioning often indicate the most relevant pieces of information.We applied novel features based on visual characteristics in addition to traditional text features and show that performance improved significantly for both the text categorization and NER tasks.

Information in visually rich formats such as PDF and HTML is often conveyed by a combination of textual and visual features. In particular, genres such as marketing flyers and info-graphics often augment textual information by its color, size, positioning, etc. As a result, tradi-tional text-based approaches to informa-tion extraction (IE) could underperform. In this study, we present a supervised ma-chine learning approach to IE from on-line commercial real estate flyers. We evaluated the performance of SVM clas-sifiers on the task of identifying 12 types

Images contained in scientific publications are widely considered useful for educational and research purposes, and their accurate indexing is critical for efficient and effective retrieval. Such image retrieval is complicated by the fact that figures in the scientific literature often combine multiple individual subfigures (panels). Multipanel figures are in fact the predominant pattern in certain types of scientific publications. The goal of this work is to automatically segment multipanel figures—a necessary step for automatic semantic indexing and in the development of image retrieval systems targeting the scientific literature. We have developed a method that uses the image content as well as the associated figure caption to: (1) automatically detect panel boundaries; (2) detect panel labels in the images and convert them to text; and (3) detect the labels and textual descriptions of each panel within the captions. Our approach combines the output of image‐content and text‐based processing steps to split the multipanel figures into individual subfigures and assign to each subfigure its corresponding section of the caption. The developed system achieved precision of 81% and recall of 73% on the task of automatic segmentation of multipanel figures.