View: Visual Information Extraction Widget for improving chart images accessibility

Chart images visually represent quantitative information. Most of these visual information are represented by graphical symbols and textual descriptions; without access to the Object Model of a graphic it is difficult for viewers to acquire the accurate underlying data. In response we propose the VIEW (Visual Information Extraction Widget), a system that automatically extracts information from raster-format charts to improve accessibility. Taking a chart image as input, the system first segments the image into connected-components and distinguishes them as graphical and textual components. By analyzing the graphical components, the system then identifies the graphic type and further conducts category-specific methods to infer the underlying data. Using the images drawn from the web, we conduct experiments to demonstrate the effectiveness of the proposed system. Based on the extracted information, VIEW generates a general-purpose descriptive data table, leading the production of multi-modal representations under the task-oriented design principle.