Xerox Corporation

Pramod Sankar Kompalli

Raja Bala

Yonghui Zhao

Michael Robert Furst

Hengzhou Ding

Shailesh Vaya

Gregory Michael Youngblood

Arjun Sharma

Disclosed are devices and methods for processing an image document in a client-server environment such that privacy of text information contained in the image document is preserved. Specifically, in a client-server environment, an image document can be processed using a local computerized device of a client to create an obfuscated document by identifying word images in the image document and scrambling those word images. The obfuscated document can be received by a server of a service provider over a network (e.g., the Internet) and processed by previously trained software (e.g., a previously trained convolutional neural network (CNN)) to recognize specific words represented by the scrambled images in the obfuscated document without having to reconstruct the image document. Since the image document is neither communicated over the network, nor reconstructed and stored on the server, privacy concerns are minimized.

Image document processing in a client-server system including privacy-preserving text recognition

Embodiments of a computer-implemented method for training a convolutional neural network (CNN) that is pre-trained using a set of color images are disclosed. The method comprises receiving a training dataset including multiple multidimensional images, each multidimensional image including a color image and a depth image; performing a fine-tuning of the pre-trained CNN using the depth image for each of the plurality of multidimensional images; obtaining a depth CNN based on the pre-trained CNN, wherein the depth CNN is associated with a first set of parameters; replicating the depth CNN to obtain a duplicate depth CNN being initialized with the first set of parameters; and obtaining a depth-enhanced color CNN based on the duplicate depth CNN being fine-tuned using the color image for each of the plurality of multidimensional images, wherein the depth-enhanced color CNN is associated with a second set of parameters.

Cross-trained convolutional neural networks using multimodal images

The disclosed embodiments illustrate methods and systems for monitoring a worker performing a crowdsourced task being presented on a computing device. The method comprises performing at least one of a facial detection processing or an eye tracking processing, on a video stream captured by a camera of the computing device. An inattention instance of the worker is determined based on at least one of the facial detection processing or the eye tracking processing. Further, the inattention instance is communicated to a crowdsourcing platform, wherein the crowdsourced task is received from the crowdsourcing platform.

Methods and systems for monitoring a worker performing a crowdsourced task

A system and method of rendering in real time a virtual object onto a viewfinder display, the method comprising determining one or more scene properties of a scene on a viewfinder display of a device, receiving a virtual object for insertion into the scene, determining a location for placing the virtual object within the scene, determining a first appearance of the virtual object based on the one or more scene properties, and inserting the virtual object with the first appearance into the scene depicted on the viewfinder display of the device based on the location.

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Hyderabad Telangana, India

Pramod Sankar Kompalli