Chart-Type Classification Using Convolutional Neural Network for Scholarly Figures

Takeo Ishihara, Kento Morita, Nobu C. Shirai, Tetsushi Wakabayashi, Wataru Ohyama

Abstract: Text-to-speech conversion by smart speakers is expected to assist visually handicapped people who are near total blindness to read documents. This research supposes a situation where such a text-to-speech conversion is applied to scholarly documents. Usually, a page in scholarly documents consists of multiple regions, i.e. ordinary text, mathematical expressions, tables, and figures. In this paper, we propose a method which classifys chart-type of scholarly figures using a convolutional neural network. The method classifies an input figure image into line charts or others. We evaluated the accuracy of the method using scholarly figures dataset collected from actual academic papers. The classification accuracy of the proposed method achieved 97%. We also compared the performance of the proposed method with that of hand-crafted features and support vector machine. The results suggest that the proposed CNN classification outperforms the conventional approach.