Graphical Object Detection in Document Images

Graphical elements: particularly tables and figures contain a visual summary of the most valuable information contained in a document. Therefore, localization of such graphical objects in the document images is the initial step to understand the content of such graphical objects or document images. In this paper, we present a novel end-to-end trainable deep learning based framework to localize graphical objects in the document images called as Graphical Object Detection (GOD). Our framework is data-driven and does not require any heuristics or meta-data to locate graphical objects in the document images. The GOD explores the concept of transfer learning and domain adaptation to handle scarcity of labeled training images for graphical object detection task in the document images. Performance analysis carried out on the various public benchmark data sets: ICDAR-2013, ICDAR-POD2017,and UNLV shows that our model yields promising results as compared to state-of-the-art techniques.Comment:

Segmentation-Based Retrieval of Document Images from Diverse Collections

We describe a methodology for retrieving document images from large extremely diverse collections. First we perform content extraction, that is the location and measurement of regions containing handwriting, machineprinted text, photographs, blank space, etc, in documents represented as bilevel, greylevel, or color images. Recent experiments have shown that even modest per-pixel content classification accuracies can support usefully high recall and precision rates (of, e.g., 80–90%) for retrieval queries within document collections seeking pages that contain a fraction of a certain type of content. When the distribution of content and error rates are uniform across the entire collection, it is possible to derive IR measures from classification measures and vice versa. Our largest experiments to date, consisting of 80 training images totaling over 416 million pixels, are presented to illustrate these conclusions. This data set is more representative than previous experiments, containing a more balanced distribution of content types. Contained in this data set are also images of text obtained from handheld digital cameras and the success of existing methods (with no modification) in classifying these images with are discussed. Initial experiments in discriminating line art from the four classes mentioned above are also described. We also discuss methodological issues that affect both ground-truthing and evaluation measures