Amazon Technologies, Inc.

Mohammed El Hamalawi

Rahul Bhotika

Amit Adam

Vijay Mahadevan

Shai Mazor

Vivek Bhadauria

Hadar Averbuch Elor

Kunwar Yashraj Singh

Shahar Tsiper

Gowtham Jeyabalan

Gal Sabina Star

Roee Litman

Amulya Srivastava

Paul H Kang

Techniques for document rectification via homography recovery using machine learning are described. An image rectification system can intelligently make use of multiple pipelines for rectifying document images based on the detected type of device that generated the images. The image rectification system can provide high-quality rectifications without requiring human cooperation, multiple views of the document in multiple images, and/or without being constrained to only be able to process images from one source context.

Document rectification via homography recovery using machine learning

A synthetic document generator that obtains a configuration for a synthetic document derived from real-world documents. The configuration specifies element templates to be included in the synthetic document and weights for the specified element templates. The system generates synthetic documents based on the configuration; the synthetic documents include diversified versions of the element templates specified in the configuration. Annotation documents are generated for the synthetic documents that include information describing the respective synthetic documents. A machine learning model for analyzing real-world documents can then be trained using the synthetic and annotation documents. Feedback from the analysis of real-world documents by the machine learning model can be used to generate a new configuration for generating additional synthetic and annotation documents which are used to further train the model.

Synthetic document generator

Growing community of inventors

Kirkland, WA, United States of America

Mohammed El Hamalawi