Using object detection to extract structured content from documents

Structured content such as figures, tables, graphs, captions, and other graphical material often capture the essence of a document. Experienced readers often review the graphical material in a document first to quickly grasp the contents of the document. It is thus evident that identifying and extracting the structured content of a document, e.g., graphical components, is important in building a deeper semantic understanding of the document. Techniques presented herein automatically extract the structured content of documents. Machine-learning techniques, e.g., object detection, computer vision, etc., are used to recognize and extract the structured content. The techniques work well regardless of the tool used to create the document. For example, the document can be a PDF file, captured via screenshot, generated by a computer-aided design tool, etc. The techniques work across fields of study, across publishing conventions, languages and written scripts, and are robust to different formats of graphical content, e.g., vector/raster graphics

Hashing for Similarity Search: A Survey

Similarity search (nearest neighbor search) is a problem of pursuing the data items whose distances to a query item are the smallest from a large database. Various methods have been developed to address this problem, and recently a lot of efforts have been devoted to approximate search. In this paper, we present a survey on one of the main solutions, hashing, which has been widely studied since the pioneering work locality sensitive hashing. We divide the hashing algorithms two main categories: locality sensitive hashing, which designs hash functions without exploring the data distribution and learning to hash, which learns hash functions according the data distribution, and review them from various aspects, including hash function design and distance measure and search scheme in the hash coding space

Towards Universal Object Detection

Object detection is one of the most important and challenging research topics in computer vision. It is playing an important role in our everyday life and has many applications, e.g. surveillance, autonomous driving, robotics, drone, medical imaging, etc. The ultimate goal of object detection is a universal object detector that can work very well in any case under any condition like human vision system. However, there are multiple challenges on the universality of object detection, e.g. scale-variance, high-quality requirement, domain shift, computational constraint, etc. These will prevent the object detector from being widely used for various scales of objects, critical applications requiring extremely accurate localization, scenarios with changing domain priors, and diverse hardware settings. To address these challenges, multiple solutions have been proposed in this thesis. These include an efficient multi-scale architecture to achieve scale-invariant detection, a robust multi-stage framework effective for high-quality requirement, a cross-domain solution to extend the universality over various domains, and a design of complexity-aware cascades and a novel low-precision network to enhance the universality under different computational constraints. All these efforts have substantially improved the universality of object detection, and the advanced object detector can be applied to broader environments

Semi-Supervised Classification Using Object Metadata

Generally, the present disclosure is directed to classification of data objects (e.g. documents, images, graphs, etc.). In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to classify a data object based on object data and/or metadata associated with the object

Suggesting Deletion of Blurry Photos

Generally, the present disclosure is directed to identifying and suggesting deletion of blurry photos. In particular, in some implementations, the systems and methods of the present disclosure can include or otherwise leverage one or more machine-learned models to predict a blurriness characteristic of an image based on image data. For example, the blurriness characteristic can describe a percentage of the image that is blurry