Google Inc.

Honda Motor Co., Ltd.

Alessandro Bissacco

Xiaohang Wang

Hartmut Neven

Henry Allan Rowley

Sanjiv Kumar

Farhan Shamsi

Yakov Okshtein

Justin Max Scheiner

Andrea Lynn Frome

Bo Wu

Ulrich Franz Buddemeier

Kong Man Cheung

Marcus Quintana Mitchell

Raymond W Smith

Debra Lin Repenning

Shlomo Reuben Urbach

Leon G Palm

Marria Nazif

Daniel Talavera

Sam Shih

Mark Leslie Snyder

Hartwig Adam

Luc Vincent

Ming-Hsuan Yang

Ahmad A Abdulkader

Glenn Berntson

Marco Zennaro

Krishnendu Chaudhury

Jose Jeronimo Moreira Rodrigues

Kishore A Papineni

Yasuhisa Fujii

Jeff Huber

German Cheung

Glen Berntson

Shangbang Long

Siyang Qin

Dmitry Panteleev

Michail Raptis

Justin Scheiner

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for jointly performing text detection and layout analysis. In one aspect, a method comprises processing the image and a set of object queries to generate an encoded representation of the image and an encoded representation of the set of object queries; processing the encoded representation of the image and the encoded representation of the set of object queries to generate a set of text detection masks; processing the encoded representation of the set of object queries to generate layout relevance measures; processing the encoded representation of the set of object queries to generate textness scores for the text detection masks; generating a text detection output that defines respective areas of the image that include text items; and generating a layout analysis output that defines clusters of respective areas of the image identified by the text detection masks.

Unified scene text detection and layout analysis

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

The technology of the present disclosure includes computer-implemented methods, computer program products, and systems to filter images before transmitting to a system for optical character recognition ('OCR'). A user computing device obtains a first image of the card from the digital scan of a physical card and analyzes features of the first image, the analysis being sufficient to determine if the first image is likely to be usable by an OCR algorithm. If the user computing device determines that the first image is likely to be usable, then the first image is transmitted to an OCR system associated with the OCR algorithm. Upon a determination that the first image is unlikely to be usable, a second image of the card from the digital scan of the physical card is analyzed. The optical character recognition system performs an optical character recognition algorithm on the filtered card.

Client side filtering of card OCR images

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.

Payment card OCR with relaxed alignment

A system and method is provided for automatically recognizing building numbers in street level images. In one aspect, a processor selects a street level image that is likely to be near an address of interest. The processor identifies those portions of the image that are visually similar to street numbers, and then extracts the numeric values of the characters displayed in such portions. If an extracted value corresponds with the building number of the address of interest such as being substantially equal to the address of interest, the extracted value and the image portion are displayed to a human operator. The human operator confirms, by looking at the image portion, whether the image portion appears to be a building number that matches the extracted value. If so, the processor stores a value that associates that building number with the street level image.

System and method of determining building numbers

Embodiments herein provide computer-implemented techniques for allowing a user computing device to extract financial card information using optical character recognition ('OCR'). Extracting financial card information may be improved by applying various classifiers and other transformations to the image data. For example, applying a linear classifier to the image to determine digit locations before applying the OCR algorithm allows the user computing device to use less processing capacity to extract accurate card data. The OCR application may train a classifier to use the wear patterns of a card to improve OCR algorithm performance. The OCR application may apply a linear classifier and then a nonlinear classifier to improve the performance and the accuracy of the OCR algorithm. The OCR application uses the known digit patterns used by typical credit and debit cards to improve the accuracy of the OCR algorithm.

Extracting card data with wear patterns

A text recognition server is configured to recognize text in a sparse text image. Specifically, given an image, the server specifies a plurality of 'patches' (blocks of pixels within the image). The system applies a text detection algorithm to the patches to determine a number of the patches that contain text. This application of the text detection algorithm is used both to estimate the orientation of the image and to determine whether the image is textually sparse or textually dense. If the image is determined to be textually sparse, textual patches are identified and grouped into text regions, each of which is then separately processed by an OCR algorithm, and the recognized text for each region is combined into a result for the image as a whole.

Text recognition for textually sparse images

Embodiments of this invention relate to detecting and blurring images. In an embodiment, a system detects objects in a photographic image. The system includes an object detector module configured to detect regions of the photographic image that include objects of a particular type at least based on the content of the photographic image. The system further includes a false positive detector module configured to determine whether each region detected by the object detector module includes an object of the particular type at least based on information about the context in which the photographic image was taken.

Object detection with false positive filtering

According to an embodiment, a method for filtering descriptors for visual object recognition is provided. The method includes identifying false positive descriptors having a local match confidence that exceeds a predetermined threshold and a global image match confidence that is less than a second threshold. The method also includes training at least one classifier to discriminate between the false positive descriptors and other descriptors. The method further includes filtering feature point matches using the at least one classifier. According to another embodiment, the filtering step may further include removing one or more feature point matches from a result set. According to a further embodiment, a system for filtering feature point matches for visual object recognition is provided. The system includes a hard false positive identifier, a classifier trainer and a hard false positive filter.

Classifying false positive descriptors

Growing community of inventors

Los Angeles, CA, United States of America

Alessandro Bissacco