Amazon Technologies, Inc.

Roee Litman

Shai Mazor

Shahar Tsiper

Raghavan Manmatha

Rahul Bhotika

Amit Adam

Vijay Mahadevan

Jonathan Wu

Oron Anschel

Hadar Averbuch Elor

Kunwar Yashraj Singh

Gal Sabina Star

Mohammed El Hamalawi

Ron Litman

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

Techniques for recognizing text in an image are described. An exemplary method may include receiving a request to recognize text in an image; extracting features from the image and generating a visual feature sequence from the extracted features; performing selective contextual refinement at least one selective contextual refinement block of a stack of selective contextual refinement blocks to generate a text prediction by: generating a contextual feature map and combining the contextual feature map with the visual feature sequence into a visual feature space, and applying a selective decoder that utilizes a two-step attention on the visual feature space to generate a text prediction, wherein the two-step attention includes performing a 1-D self-attention computation to generate attentional features and decoding the attentional features to generate the text prediction; and outputting the generated text prediction.

Residual context refinement network architecture for optical character recognition

Growing community of inventors

Petach-Tikva, Israel

Roee Litman