Amazon Technologies, Inc.

Microsoft Technology Licensing, LLC

Magic Leap, Inc.

Shai Mazor

Amit Adam

Rahul Bhotika

Oron Anschel

Hadar Averbuch Elor

Stefano Soatto

Shahar Tsiper

Andrea Olgiati

Sebastian Nowozin

Or Perel

Wendy Tse

Gal Sabina Star

Omer Yair

Giora Yahav

David Cohen

Erez Tadmor

Assaf Pellman

Ron Litman

Roee Litman

Raghavan Manmatha

Bhavesh Anil Doshi

Vijay Mahadevan

Jonathan Wu

Anand Dhandhania

Umar Farooq

Kunwar Yashraj Singh

Patrick Ian Wilson

Yair Kittenplon

Mohammed El Hamalawi

Niv Berchi Nayman

Omri Ben-Eliezer

Gururaj Kosuru

Michael Schober

A document layout system for determining a layout of a document. The document layout system is configured to apply an OCR technique to identify the text in the document. The document layout system is further configured to generate a graph representation of the document, wherein the graph representation comprises a plurality of nodes and a plurality of edges that connect different ones of the plurality of nodes, wherein individual ones of the nodes correspond to different portions of the text. The document layout system is also configured to apply a graph cluster network machine learning model to the graph representation to identify a layout of different sections of the document according to respective merge inferences determined for individual ones of the plurality of edges. The document layout system is also configured to provide the layout of different sections of the document.

Graph-based document layout detection

A method for determining a distance to a target object includes transmitting light pulses to illuminate the target object and sensing, in a first region of a light-sensitive pixel array, light provided from an optical feedback device that receives a portion of the transmitted light pulses. The feedback optical device includes a preset reference depth. The method includes calibrating time-of-flight (TOF) depth measurement reference information based on the sensed light in the first region of the pixel array. The method further includes sensing, in a second region of the light-sensitive pixel array, light reflected from the target object from the transmitted light pulses. The distance of the target object is determined based on the sensed reflected light and the calibrated TOF measurement reference information.

Real time calibration for time-of-flight depth measurement

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

Techniques for auto-generation of annotated real-world training data are described. An electronic document is analyzed to determine text represented in the document and corresponding locations of the text. A representation of the electronic document is modified to include markers and printed. The printed document is photographed in real-world environments, and the markers within the digital photographs are analyzed to allow for the depiction of the document within the photographs to be rectified. The text and location data are used to annotate the rectified images.

Auto-annotation techniques for text localization

Techniques for grammar-based automated generation of annotated synthetic form training data for machine learning are described. A training data generation engine utilizes a defined grammar to construct a layout for a form, select key-value units to place within the layout, and select attribute variants for the key-value units. The form is rendered and stored at a storage location, where it can be provided along with other similarly-generated forms to be used as training data for a machine learning model.

Grammar-based automated generation of annotated synthetic form training data for machine learning

Techniques for layout-agnostic complex document processing are described. A document processing service can analyze documents that do not adhere to defined layout rules in an automated manner to determine the content and meaning of a variety of types of segments within the documents. The service may chunk a document into multiple chunks, and operate upon the chunks in parallel by identifying segments within each chunk, classifying the segments into segment types, and processing the segments using special-purpose analysis engines adapted for the analysis of particular segment types to generate results that can be aggregated into an overall output for the entire document that captures the meaning and context of the document text.

Layout-agnostic complex document processing system

Techniques for automated form understanding via layout-agnostic identification of keys and corresponding values are described. An embedding generator creates embeddings of pixels from an image including a representation of a form. The generated embeddings are similar for pixels within a same key-value unit, and far apart for pixels not in a same key-value unit. A weighted bipartite graph is constructed including a first set of nodes corresponding to keys of the form and a second set of nodes corresponding to values of the form. Weights for the edges are determined based on an analysis of distances between ones of the embeddings. The graph is partitioned according to a scheme to identify pairings between the first set of nodes and the second set of nodes that produces a minimum overall edge weight. The pairings indicate keys and values that are associated within the form.

Automated form understanding via layout agnostic identification of keys and corresponding values

Techniques for layout-agnostic clustering-based classification of document keys and values are described. A key-value differentiation unit generates feature vectors corresponding to text elements of a form represented within an electronic image using a machine learning (ML) model. The ML model was trained utilizing a loss function that separates keys from values. The feature vectors are clustered into at least two clusters, and a cluster is determined to include either keys of the form or values of the form via identifying neighbors between feature vectors of the cluster(s) with labeled feature vectors.

Layout-agnostic clustering-based classification of document keys and values

A depth detection apparatus is described which has a memory storing raw time-of-flight sensor data received from a time-of-flight sensor. The depth detection apparatus also has a trained machine learning component having been trained using training data pairs. A training data pair comprises at least one simulated raw time-of-flight sensor data value and a corresponding simulated ground truth depth value. The trained machine learning component is configured to compute in a single stage, for an item of the stored raw time-of-flight sensor data, a depth value of a surface depicted by the item, by pushing the item through the trained machine learning component.

Depth from time-of-flight using machine learning

A depth detection apparatus is described which has a memory and a computation logic. The memory stores frames of raw time-of-flight sensor data received from a time-of-flight sensor, the frames having been captured by a time-of-flight camera in the presence of motion such that different ones of the frames were captured using different locations of the camera and/or with different locations of an object in a scene depicted in the frames. The computation logic has functionality to compute a plurality of depth maps from the stream of frames, whereby each frame of raw time-of-flight sensor data contributes to more than one depth map.

Temporal time-of-flight

Growing community of inventors

Binyamina, Israel

Shai Mazor