Abstract: A data processing system implements a method for training machine learning modes, including receiving a set of one or more unlabeled documents associated one or more first categories of documents to be used to train machine learning models to analyze the one or more unlabeled documents, and fine-tuning a first machine learning model and a second machine learning model based on the one or more unlabeled document to enable the first machine learning model to determine a semantic representation of the one or more first categories of document, and to enable the second machine learning model to classify the semantic representations according to the one or more first categories of documents, the first machine learning model and the second machine learning model having been trained using first unlabeled training data including a second plurality of categories of documents that do not include the one or more first categories of documents.

Abstract: The disclosure herein describes providing signature data of an input document. Text data of the input document is obtained (e.g., OCR data generated from image data) and a first set of signature fields are identified using signature key-value pairs of the text data. A first subset of signed signature fields and a first subset of unsigned signature fields are determined based on mapping to a set of predicted values. A second set of signature fields are determined using a region prediction model applied to image data of the input document. Region images associated with the first subset of unsigned signature fields and with second set of signature fields are obtained and a second set of signed signature fields and a second set of unsigned signature fields are determined using a signature recognition model. Signature output data is provided including signed signature fields and/or unsigned signature fields.

Abstract: Interfaces and systems are provided for harvesting ground truth from forms to be used in training models based on key-value pairings in the forms and to later use the trained models to identify related key-value pairings in new forms. Initially, forms are identified and clustered to identify a subset of forms to label with the key-value pairings. Users provide input to identify keys to use in labeling and then select/highlight text from forms that are presented concurrently with the keys in order to associate the highlighted text with the key(s) as the corresponding key-value pairing(s). After labeling the forms with the key-value pairings, the key-value pairing data is used as ground truth for training a model to independently identify the key-value pairing(s) in new forms. Once trained, the model is used to identify the key-value pairing(s) in new forms.

Abstract: A method for customizing an optical character recognition system is disclosed. The optical character recognition system includes a general-purpose decoder configured to convert character images, recognized in a digital image, into text based on a general-purpose text structure. An application-specific customization is received. The application-specific customization includes an application-specific text structure that differs from the general-purpose text structure. A customized model is generated based on the application-specific customization. An enhanced application-specific decoder is generated by modifying the general-purpose decoder to, during run-time execution of the optical character recognition system, leverage the customized model to convert character images demonstrating the application-specific text structure into text.

Abstract: The disclosed technology is generally directed to optical character recognition for forms. In one example of the technology, optical character recognition is performed on a plurality of forms. The forms of the plurality of forms include at least one type of form. Anchors are determined for the forms, including corresponding anchors for each type of form of the plurality of forms. Feature rules are determined, including corresponding feature rules for each type of form of the plurality of forms. Features and labels are determined for each form of the plurality of forms. A training model is generated based on a ground truth that includes a plurality of key-value pairs corresponding to the plurality of forms, and further based on the determined features and labels for the plurality of forms.

Abstract: The disclosed technology is generally directed to optical text recognition for forms. In one example of the technology, line grouping rules are generated based on the generic forms and a ground truth for the generic forms. Line groupings are applied to the generic forms based on the line grouping rules. Feature extraction rules are generated. Features are extracted from the generic forms based on the feature extraction rules. A key-value classifier model is generated, such that the key-value classifier model is configured to determine, for each line of a form: a probability that the line is a value, and a probability that the line is a key. A key-value pairing model is generated, such that the key-value pairing model is configured to predict, for each key in a form, which value in the form corresponds to the key.

Abstract: Interfaces and systems are provided for harvesting ground truth from forms to be used in training models based on key-value pairings in the forms and to later use the trained models to identify related key-value pairings in new forms. Initially, forms are identified and clustered to identify a subset of forms to label with the key-value pairings. Users provide input to identify keys to use in labeling and then select/highlight text from forms that are presented concurrently with the keys in order to associate the highlighted text with the key(s) as the corresponding key-value pairing(s). After labeling the forms with the key-value pairings, the key-value pairing data is used as ground truth for training a model to independently identify the key-value pairing(s) in new forms. Once trained, the model is used to identify the key-value pairing(s) in new forms.

Abstract: The disclosed technology is generally directed to optical text recognition for forms. In one example of the technology, line grouping rules are generated based on the generic forms and a ground truth for the generic forms. Line groupings are applied to the generic forms based on the line grouping rules. Feature extraction rules are generated. Features are extracted from the generic forms based on the feature extraction rules. A key-value classifier model is generated, such that the key-value classifier model is configured to determine, for each line of a form: a probability that the line is a value, and a probability that the line is a key. A key-value pairing model is generated, such that the key-value pairing model is configured to predict, for each key in a form, which value in the form corresponds to the key.

Abstract: The disclosed technology is generally directed to optical character recognition for forms. In one example of the technology, optical character recognition is performed on a plurality of forms. The forms of the plurality of forms include at least one type of form. Anchors are determined for the forms, including corresponding anchors for each type of form of the plurality of forms. Feature rules are determined, including corresponding feature rules for each type of form of the plurality of forms. Features and labels are determined for each form of the plurality of forms. A training model is generated based on a ground truth that includes a plurality of key-value pairs corresponding to the plurality of forms, and further based on the determined features and labels for the plurality of forms.

Abstract: A method includes obtaining data representing multiple characters, determining a code for each character wherein each code corresponds to a different audio frequency, and transmitting the codes at the corresponding audio frequencies.

Abstract: An apparatus that automatically monitors a display device includes a photo sensor configured to receive light from a display screen of the display device. The photo sensor provides signals representing detected light levels to a processor. The processor is coupled to the display device and is configured to cause the display device to present a test sequence including a plurality of images on the display screen. The processor is configured to capture data from the photo sensor during the presentation of the test sequence and to compare the captured data to an expected sequence corresponding to the test sequence displayed by a well-functioning display. The processor is further configured to report any mismatch between the captured data and the expected sequence as a possible malfunction of the display device.

Abstract: A method includes obtaining data representing multiple characters, determining a code for each character wherein each code corresponds to a different audio frequency, and transmitting the codes at the corresponding audio frequencies.

Abstract: A template and/or knowledge associated with a synchronous meeting are obtained by a computing device. The computing device then adaptively manages the synchronous meeting based at least in part on the template and/or knowledge.

Abstract: The claimed subject matter relates to an architecture to produce disincentives to wearing counterfeit or stolen merchandise in public. In particular, the architecture utilizes a unique identifier associated with each unit of the product, and provides both a registration channel for receiving ownership registration and a verification channel to receive requests for verification. By way of illustration, the architecture can include associating a brand logotype that includes unique markings with each unit of a product, a private web service where the retailer may upload customer information at the time of sale, and a publicly available web service, where a third party may inquire about the ownership of a product containing a certain unique identifier.

Abstract: Techniques and technologies are described herein for motion parallax three-dimensional (3D) imaging. Such techniques and technologies do not require special glasses, virtual reality helmets, or other user-attachable devices. More particularly, some of the described motion parallax 3D imaging techniques and technologies generate sequential images, including motion parallax depictions of various scenes derived from clues in views obtained of or created for the displayed scene.

Abstract: Techniques and technologies are described herein for motion parallax three-dimensional (3D) imaging. Such techniques and technologies do not require special glasses, virtual reality helmets, or other user-attachable devices. More particularly, some of the described motion parallax 3D imaging techniques and technologies generate sequential images, including motion parallax depictions of various scenes derived from clues in views obtained of or created for the displayed scene.

Abstract: Techniques and technologies are described herein for motion parallax three-dimensional (3D) imaging. Such techniques and technologies do not require special glasses, virtual reality helmets, or other user-attachable devices. More particularly, some of the described motion parallax 3D imaging techniques and technologies generate sequential images, including motion parallax depictions of various scenes derived from clues in views obtained of or created for the displayed scene.

Abstract: Described is modeling a room to obtain estimates for walls and a ceiling, and using the model to improve sound source localization by incorporating reflection (reverberation) data into the location estimation computations. In a calibration step, reflections of a known sound are detected at a microphone array, with their corresponding signals processed to estimate wall (and ceiling) locations. In a sound source localization step, when an actual sound (including reverberations) is detected, the signals are processed into hypotheses that include reflection data predictions based upon possible locations, given the room model. The location corresponding to the hypothesis that matches (maximum likelihood) the actual sound data is the estimated location of the sound source.

Abstract: A template and/or knowledge associated with a synchronous meeting are obtained by a computing device. The computing device then adaptively manages the synchronous meeting based at least in part on the template and/or knowledge.

Abstract: Techniques and technologies are described herein for motion parallax three-dimensional (3D) imaging. Such techniques and technologies do not require special glasses, virtual reality helmets, or other user-attachable devices. More particularly, some of the described motion parallax 3D imaging techniques and technologies generate sequential images, including motion parallax depictions of various scenes derived from clues in views obtained of or created for the displayed scene.