Amazon Technologies, Inc.

Utkarsh Prateek

Avnish Sikka

Sonjeev Jahagirdar

Emilie Noelle McConville

Yue Liu

Matthew Joseph Cole

David Paul Ramos

Matthew Daniel Hart

Ankur Datta

Bhavesh Anil Doshi

Prashant Hegde

Laura Varnum Finney

Michael Vanne

Daniel Makoto Wilenson

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

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

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

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Natick, MA, United States of America

Utkarsh Prateek