Google Inc.

Henry Allan Rowley

Sanjiv Kumar

Xiaohang Wang

Farhan Shamsi

Shumeet Baluja

Yakov Okshtein

Marcus Quintana Mitchell

Charles Joseph Rosenberg

Jose Jeronimo Moreira Rodrigues

Yushi Jing

Debra Lin Repenning

Thomas Deselaers

Alessandro Bissacco

Radhika Malpani

Franz Josef Och

Jay Yagnik

Sergey Ioffe

Daniel M Keysers

Jeff Huber

Zhifei Li

Yang Song

Hartwig Adam

Ullas Gargi

Yang Li

Ming Zhao

Ashok Chhabedia Popat

Aparna Chennapragada

Michael Jahr

Justin Max Scheiner

Leon G Palm

Ting Liu

Ameesh Makadia

Arungundram C Narendran

Jesse Berent

Andrii Maksai

Li-Lun Wang

Jose Moreira Rodrigues

Minyoung Kim

Claudiu Musat

Victor Carbune

Anelia Angelova

Peter Hazlehurst

Kishore A Papineni

Marcos Calvo

Francisco Jose Claude Faust

Systems and methods for editing and generating digital ink. The present technology may provide systems and methods for training a handwriting model to generate digital ink that is stylistically and visually consistent with an original handwriting input, but which incorporates one or more changes to the text of the original handwriting input. In some examples, training may be performed using training examples that include an original handwriting sample and an original label representing the sequence of characters in the original handwriting sample. In such a case, the original handwriting sample may be processed to generate a style vector that is randomly masked, and the handwriting model may then be trained to generate a predicted handwriting sample that closely matches the original handwriting sample using the masked style vector and the original label as inputs.

Systems and methods for digital ink generation and editing

A computer-implemented method for generating results for a client-requested query involves receiving a query produced by a client communication device, generating a result for the query in response to reception of the query, determining one or more predictive follow-up requests before receiving an actual follow-up request from the client device, and initiating retrieval of information associated with the one or more predictive follow-up requests, and transmitting at least part of the result to the client device, and then transmitting to the client device at least part of the information associated with the one or more predictive follow-up requests.

Predictive information retrieval

A computer-implemented method of providing personalized route information involves gathering a plurality of past location indicators over time for a wireless client device, determining a future driving objective using the plurality of previously-gathered location indicators, obtaining real-time traffic data for an area proximate to the determined driving objective, and generating a suggested route for the driving objective using the near real-time traffic data.

Transportation routing

Comparing extracted card data from a continuous scan comprises receiving, by one or more computing devices, a digital scan of a card; obtaining a plurality of images of the card from the digital scan of the physical card; performing an optical character recognition algorithm on each of the plurality of images; comparing results of the application of the optical character recognition algorithm for each of the plurality of images; determining if a configured threshold of the results for each of the plurality of images match each other; and verifying the results when the results for each of the plurality of images match each other. Threshold confidence level for the extracted card data can be employed to determine the accuracy of the extraction. Data is further extracted from blended images and three-dimensional models of the card. Embossed text and holograms in the images may be used to prevent fraud.

Comparing extracted card data using continuous scanning

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for associating still images and videos. One method includes receiving a plurality of images and a plurality of videos and determining whether the images are related to the videos. The determining includes, for an image and a video, extracting features from the image and extracting features frames of the video, and comparing the features to determine whether the image is related to the video. The method further includes maintaining a data store storing data associating each image with each video determined to be related to the image.

Associating still images and videos

A computer-implemented method includes: receiving, at a user device, user input corresponding to handwritten text to be recognized using a recognition engine; and receiving, at the user device, a representation of the handwritten text. The representation includes the handwritten text parsed into individual handwritten characters. The method further includes: displaying, on a display of the user device, the handwritten characters using a first indicator; receiving, at the user device, an identification of a text character recognized as one of the handwritten characters; displaying, on the display, the text character; and adjusting, at the user device, the one of the handwritten characters from being displayed using the first indicator to using a second indicator in response to the received identification. The first and second indicators are different.

Progress display of handwriting input

Capturing information from an imprinted item comprises a computing device that receives an image of a back side of an imprinted item, the payment instrument comprising information imprinted thereon such that the imprinted information protrudes from a front side of the imprinted item and the imprinted information is indented into the back side of the imprinted item. The computing device extracts sets of characters from the image of the back side of the imprinted item based on the imprinted information indented into the back side of the imprinted item and depicted in the image of the back side of the imprinted item. The computing device applies a first character recognition application to process the sets of characters extracted from the image of the back side of the imprinted item and categorizes each of the sets of characters into one of a plurality of categories relating to the information.

Extraction of data from a digital image

Extracting financial card information with relaxed alignment comprises a method to receive an image of a card, determine one or more edge finder zones in locations of the image, and identify lines in the one or more edge finder zones. The method further identifies one or more quadrilaterals formed by intersections of extrapolations of the identified lines, determines an aspect ratio of the one or more quadrilateral, and compares the determined aspect ratios of the quadrilateral to an expected aspect ratio. The method then identifies a quadrilateral that matches the expected aspect ratio and performs an optical character recognition algorithm on the rectified model. A similar method is performed on multiple cards in an image. The results of the analysis of each of the cards are compared to improve accuracy of the data.

Extracting card data from multiple cards

A first handwriting input is received comprising strokes corresponding to a set of first characters comprising one or more first characters forming a first language model unit. A set of candidate first characters and a set of candidate first language model units with corresponding probability scores are determined based on an analysis of the one or more sets of candidate first characters using the first language model and a corresponding first character recognition model. When no first probability score satisfies a threshold, one or more sets of candidate second characters and a set of candidate second language model units are determined based on an analysis of the first handwriting input using a second language model and a corresponding second character recognition model. A first candidate list is then output comprising at least one of the set of candidate second language model units.

Techniques for scheduling language models and character recognition models for handwriting inputs

Growing community of inventors

Sunnyvale, CA, United States of America

Henry Allan Rowley