Amazon Technologies, Inc.

International Business Machines Corporation

Sonjeev Jahagirdar

Avnish Sikka

Matthew Joseph Cole

Emilie Noelle McConville

Utkarsh Prateek

Yue Liu

David Paul Ramos

Moshe Emanuel Matsa

Junxiong Jia

Matthew Daniel Hart

Oleg Levin

Ankur Datta

Oleg Rybakov

Bhavesh Anil Doshi

Rakesh Madhavan Nambiar

Matias Omar Gregorio Benitez

Prashant Hegde

Neil David Cooper

Laura Varnum Finney

Christopher John Lish

Michael Vanne

Daniel Makoto Wilenson

James Sassano

Pengcheng Wu

Nicholas Randal Sovich

A system for recognizing objects and/or text in image data may use context data to perform object/text recognition. The system may also use context data when determining potential functions to execute in response to recognizing the object/text. Context data may be gathered based on device sensor data, user profile data such as the behavior of a user or the behavior of those in a user's social network, or other factors. Recognition processing and/or function selection may be configured to account for context data when operating to improve output results.

Item recognition using context data

Disclosed are techniques for merging optical character recognized (OCR'd) text from frames of image data. In some implementations, a device sends frames of image data to a server, where each frame includes at least a portion of a captured textual item. The server performs optical character recognition (OCR) on the image data of each frame. When OCR'd text from respective frames is returned to the device from the server, the device can perform matching operations on the text, for instance, using bounding boxes and/or edit distance processing. The device can merge any identified matches of OCR'd text from different frames. The device can then display the merged text with any corrections.

Merging optical character recognized text from frames of image data

Various embodiments describe systems and methods for utilizing a reduced amount of processing capacity for incoming data over time, and, in response to detecting a scene-change-event, notify one or more data processors that a scene-change-event has occurred, and cause incoming data to be processed as new data. In some embodiments, an incoming frame can be compared with a reference frame to determine a difference between the reference frame and the incoming frame. The reference frame may be correlated to a latest scene-change-event. In response to a determination that the difference does not meet one or more difference criteria, a user interface or at least one processor of the computing device can be notified to reduce processing of incoming data over time. In response to a determination that the difference meets the one or more difference criteria, the user interface or the at least one processor can be notified that a scene-change-event has occurred. Incoming data to the computing device is then treated as new and processed as those under an active condition. The current incoming frame can be selected as a new reference frame for detecting next scene-change-event.

Visual and audio recognition for scene change events

Disclosed are techniques for recognizing text from one or more frames of image data using contextual information. In some implementations, image data including a captured textual item is processed to identify an entity in the image data. A context can be selected using the entity, where the context corresponds to a dictionary. Text in the captured textual item can be identified using the dictionary. The identified text can be output to a display device.

Recognizing text from frames of image data using contextual information

Embodiments of the subject technology provide for a hybrid OCR approach which combines server and device side processing that can offset disadvantages of performing OCR solely on the server side or the device side. More specifically, the subject technology utilizes image characteristics such as glyph details and image quality measurements to opportunistically schedule OCR processing on the mobile device and/or server. In this regard, text extracted by a 'faster' OCR engine (e.g., one with less latency) is displayed to a user, which is then updated by the result of a more accurate OCR engine (e.g., an OCR engine provided by the server). This approach allows factoring in additional parameters such as network latency and user preference for making scheduling decisions. Thus, the subject technology may provide significant gains in terms of reduced latency and increased accuracy by implementing one or more techniques associated with this hybrid OCR approach.

Hybrid optical character recognition

Various embodiments crowd source images to cover various angles, zoom levels, and elevations of objects and/or points of interest (POIs) while under various lighting conditions. The crowd sourced images are tagged or associated with a particular POI or geographic location and stored in a database for use by an augmented reality (AR) application to recognize objects appearing in a live view of a scene captured by at least one camera of a computing device. The more comprehensive the database, the more accurately an object or POI in the scene will be recognized and/or tracked by the AR application. Accordingly, the more accurately an object is recognized and tracked by the AR application, the more smoothly and continuous the content and movement transitions thereof can be presented to users in the live view.

Graphical refinement for points of interest

Various embodiments enable a computing device to incorporate frame selection or preprocessing techniques into a text recognition pipeline in an attempt to improve text recognition accuracy in various environments and situations. For example, a mobile computing device can capture images of text using a first camera, such as a rear-facing camera, while capturing images of the environment or a user with a second camera, such as a front-facing camera. Based on the images captured of the environment or user, one or more image preprocessing parameters can be determined and applied to the captured images in an attempt to improve text recognition accuracy.

Using a front-facing camera to improve OCR with a rear-facing camera

Processes, devices, and articles of manufacture having provisions to monitor and track multi-core Central Processor Unit resource allocation and deallocation in real-time are provided. The allocation and deallocation may be tracked by two counters with the first counter incrementing up or down depending upon the allocation or deallocation at hand, and with the second counter being updated when the first counter value meets or exceeds a threshold value.

Real-time distributed monitoring of local and global processor resource allocations and deallocations

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Cambridge, MA, United States of America

Sonjeev Jahagirdar