Abstract:
The present disclosure relates to generating computer searchable text from digital images that depict documents utilizing an orientation neural network and/or text prediction neural network. For example, one or more embodiments detect digital images that depict documents, identify the orientation of the depicted documents, and generate computer searchable text from the depicted documents in the detected digital images. In particular, one or more embodiments train an orientation neural network to identify the orientation of a depicted document in a digital image. Additionally, one or more embodiments train a text prediction neural network to analyze a depicted document in a digital image to generate computer searchable text from the depicted document. By utilizing the identified orientation of the depicted document before analyzing the depicted document with a text prediction neural network, the disclosed systems can efficiently and accurately generate computer searchable text for a digital image that depicts a document.
Abstract:
The present disclosure is directed toward systems and methods that efficiently and effectively generate an enhanced document image of a displayed document in an image frame captured from a live image feed. For example, systems and methods described herein apply a document enhancement process to a displayed document in an image frame that result in an enhanced document image that is cropped, rectified, un-shadowed, and with dark text against a mostly white background. Additionally, systems and method described herein determine whether a stored digital content item includes a displayed document. In response to determining that a stored digital content item does include a displayed document, systems and methods described herein generate an enhanced document image of a displayed document included in the stored digital content item.
Abstract:
One or more embodiments of a digital content system allow a user to conveniently search and/or navigate through a collection of digital content items. In particular, a user can interact with a client device to search for and identify one or more digital content items within a collection of digital content items. For example, the digital content system may provide a photo from a collection of photos via a graphical user interface. The digital content system can receive a user input identifying a selection of one or more visual features within the photo. Based on the selected visual feature(s), the digital content system may identify photos from the collection of photos that include the identified visual feature(s) and provide access to the identified photos via the graphical user interface.
Abstract:
The present disclosure is directed toward systems and methods that enable more accurate digital object classification. In particular, disclosed systems and methods address inaccuracies in digital object classification introduced by variations in classification scores. Specifically, in one or more embodiments, disclosed systems and methods generate probability functions utilizing digital test objects and transform classifications scores into normalized classification scores utilizing probability functions. Disclosed systems and methods utilize normalized classification scores to more accurately classify and identify digital objects in a variety of applications.
Abstract:
One or more embodiments of a digital content system allow a user to conveniently search and/or navigate through a collection of digital content items. In particular, a user can interact with a client device to search for and identify one or more digital content items within a collection of digital content items. For example, the digital content system may provide a photo from a collection of photos via a graphical user interface. The digital content system can receive a user input identifying a selection of one or more visual features within the photo. Based on the selected visual feature(s), the digital content system may identify photos from the collection of photos that include the identified visual feature(s) and provide access to the identified photos via the graphical user interface.
Abstract:
The present disclosure is directed toward systems and methods to quickly and accurately identify boundaries of a displayed document in a live camera image feed, and provide a document boundary indicator within the live camera image feed. For example, systems and methods described herein utilize different display document detection processes in parallel to generate and provide a document boundary indicator that accurately corresponds with a displayed document within a live camera image feed. Thus, a user of the mobile computing device can easily see whether the document identification system has correctly identified the displayed document within the camera viewfinder feed.
Abstract:
The present disclosure is directed toward systems and methods that efficiently and effectively generate an enhanced document image of a displayed document in an image frame captured from a live image feed. For example, systems and methods described herein apply a document enhancement process to a displayed document in an image frame that result in an enhanced document image that is cropped, rectified, un-shadowed, and with dark text against a mostly white background. Additionally, systems and method described herein determine whether a stored digital content item includes a displayed document. In response to determining that a stored digital content item does include a displayed document, systems and methods described herein generate an enhanced document image of a displayed document included in the stored digital content item.
Abstract:
The present disclosure is directed toward systems and methods that enable more accurate digital object classification. In particular, disclosed systems and methods address inaccuracies in digital object classification introduced by variations in classification scores. Specifically, in one or more embodiments, disclosed systems and methods generate probability functions utilizing digital test objects and transform classifications scores into normalized classification scores utilizing probability functions. Disclosed systems and methods utilize normalized classification scores to more accurately classify and identify digital objects in a variety of applications.
Abstract:
The present disclosure is directed toward systems and methods that enable more accurate digital object classification. In particular, disclosed systems and methods address inaccuracies in digital object classification introduced by variations in classification scores. Specifically, in one or more embodiments, disclosed systems and methods generate probability functions utilizing digital test objects and transform classifications scores into normalized classification scores utilizing probability functions. Disclosed systems and methods utilize normalized classification scores to more accurately classify and identify digital objects in a variety of applications.
Abstract:
One or more embodiments of a digital content system allow a user to conveniently search and/or navigate through a collection of digital content items. In particular, a user can interact with a client device to search for and identify one or more digital content items within a collection of digital content items. For example, the digital content system may provide a photo from a collection of photos via a graphical user interface. The digital content system can receive a user input identifying a selection of one or more visual features within the photo. Based on the selected visual feature(s), the digital content system may identify photos from the collection of photos that include the identified visual feature(s) and provide access to the identified photos via the graphical user interface.