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, traditional text-based approaches to information extraction (IE) could underperform. In this study, we present a supervised machine learning approach to IE from online commercial real estate flyers. We evaluated the performance of SVM classifiers on the task of identifying 12 types of named entities using a combination of textual and visual features. Results show that the addition of visual features such as color, size, and positioning significantly increased classifier performance.
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