Amazon Technologies, Inc.

Hao Wu

Meng Wang

Jonathan Wu

Rahul Bhotika

Joseph Patrick Tighe

Hao Chen

Wei Xia

Shuo Yang

Kaustav Kundu

Song Cao

Michael Quang Thai Lam

Hao Li

Xinyu Li

Manchen Wang

Personal equipment detection may utilize pose-based detection. Input image data may be evaluated to detect persons in the image data. For detected persons, regions of the persons may be determined. Personal equipment may be detected for the detected persons in the image data and compared with the regions of the persons to determine whether the detected personal equipment is properly placed on the person.

Pose-based personal equipment detection

Methods, systems, and computer-readable media for content moderation using object detection and image classification are disclosed. A content moderation system performs object detection on an input image using one or more object detectors. The object detection finds one or more elements in the input image. The content moderation system performs classification based at least in part on the input image using one or more image classifiers. The classification determines one or more values indicative of one or more content types in the input image. The content moderation system determines one or more scores for one or more content labels corresponding to the one or more content types. At least one of the scores represents a finding of one or more of the content types in the input image. The content moderation system generates output indicating the finding of the one or more of the content types.

Content moderation using object detection and image classification

Techniques for keypoint-based multi-label word segmentation and localization are described. A machine learning model identifies bounding regions of text within an image, and then generates multiple channel matrices representing predicted keypoints of the text within the bounding regions. The keypoints can be used to rectify the corresponding graphical content from the image including the text to improve the ability to perform optical character recognition and identify the text. Line and word segmentation and localization can be performed together.

Keypoint-based multi-label word segmentation and localization

Embodiments of the present disclosure provide systems and processes for automatically determining a layout of text within an image that makes sense from a semantic perspective. In certain embodiments, the systems disclosed herein receive bounding box information relating to one or more bounding boxes that surround text within the image. The systems compare the received bounding box information to determine a clustering of bounding boxes that have an above threshold probability of including words that when read in order make sense semantically. For example, systems herein can determine whether words in a cluster correspond to a line of text.

Semantic ordering of image text

A method and system for analyzing text in an image is disclosed. A text localization and classification system accesses an annotated image comprising a plurality of text location identifiers for a given item of text. A neural network predicts the location of the given item of text using at least a first location identifier and a second location identifier. Optionally, the first location identifier comprises a first shape and the second location identifier comprises a second shape. A first loss is generated using a first loss function, the first loss corresponding to the predicated location using the first location identifier. A second loss is generated using a second loss function, the second loss corresponding to the predicated location using the second location identifier. The neural network is enhanced with backpropagation using the first loss and the second loss.

Image text localization

Growing community of inventors

Seattle, WA, United States of America

Hao Wu