Abstract
We introduce a new dataset for graphical object detection in business documents, more specifically annual reports. This dataset, iiit-ar-13k, is created by manually annotating the bounding boxes of graphical or page objects in publicly available annual reports. This dataset contains a total of 13k annotated page images with objects in five different popular categories—table, figure, natural image, logo, and signature. It is the largest manually annotated dataset for graphical object detection. Annual reports created in multiple languages for several years from various companies bring high diversity into this dataset. We benchmark iiit-ar-13k dataset with two state of the art graphical object detection techniques using faster r-cnn [20] and mask r-cnn [11] and establish high baselines for further research. Our dataset is highly effective as training data for developing practical solutions for graphical object detection in both business documents and technical articles. By training with iiit-ar-13k, we demonstrate the feasibility of a single solution that can report superior performance compared to the equivalent ones trained with a much larger amount of data, for table detection. We hope that our dataset helps in advancing the research for detecting various types of graphical objects in business documents (http://cvit.iiit.ac.in/usodi/iiitar13k.php).
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Mondal, A., Lipps, P., Jawahar, C.V. (2020). IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents. In: Bai, X., Karatzas, D., Lopresti, D. (eds) Document Analysis Systems. DAS 2020. Lecture Notes in Computer Science(), vol 12116. Springer, Cham. https://doi.org/10.1007/978-3-030-57058-3_16
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