Abstract: Systems of the present disclosure allow fields and labels to be identified in a digital image of a form without performing OCR. A digital image of a form can be partitioned into image segments using computer-vision image-segmentation techniques. Features for each image segment can be extracted using computer-vision feature-detection methods. The features extracted from an image segment can be included in an input instance for a machine-learning model. The machine-learning model can assign a classification to the input instance. The classification can associate the input instance with a field type or a label type.

Abstract: The disclosed embodiments provide a system that describes a semi-structured document for the purpose of acquiring a set of data elements from the semi-structured document. During operation, the system obtains a physics model of a semi-structured document, wherein the physics model includes a set of relationships represented by physical objects that describe relative positions of a set of data elements in the semi-structured document. Next, the system applies the physics model to a representation of the semi-structured document to automatically extract a set of data from the representation. The system then provides the extracted set of data for use with one or more applications without requiring manual input of the data into the one or more applications.

Abstract: The disclosed embodiments provide a system that describes a semi-structured document for the purpose of acquiring a set of data elements from the semi-structured document. During operation, the system obtains a physics model of a semi-structured document, wherein the physics model includes a set of relationships represented by physical objects that describe relative positions of a set of data elements in the semi-structured document. Next, the system applies the physics model to a representation of the semi-structured document to automatically extract a set of data from the representation. The system then provides the extracted set of data for use with one or more applications without requiring manual input of the data into the one or more applications.

Abstract: Systems of the present disclosure allow fields and labels to be identified in a digital image of a form without performing OCR. A digital image of a form can be partitioned into image segments using computer-vision image-segmentation techniques. Features for each image segment can be extracted using computer-vision feature-detection methods. The features extracted from an image segment can be included in an input instance for a machine-learning model. The machine-learning model can assign a classification to the input instance. The classification can associate the input instance with a field type or a label type.

Abstract: Systems of the present disclosure allow fields and labels to be identified in a digital image of a form without performing OCR. A digital image of a form can be partitioned into image segments using computer-vision image-segmentation techniques. Features for each image segment can be extracted using computer-vision feature-detection methods. The features extracted from an image segment can be included in an input instance for a machine-learning model. The machine-learning model can assign a classification to the input instance. The classification can associate the input instance with a field type or a label type.

Abstract: The disclosed embodiments relate to a system and method for calibrating optical character recognition (OCR) processes for an image captured through a mobile computing device. During operation, the system adjusts the OCR process through pre-recognition functions, OCR functions and/or post-recognition functions with multiple sets of parameter settings. With each of these sets, the system scores the OCR process output against an image with known text. Once the sets are scored, the system sorts the sets of parameters, removes some sets, then mixes and mutates the remaining sets in a process akin to evolutionary biology. By repeating this procedure, the system produces a set of parameter settings that can be used to calibrate OCR processing.

Abstract: The disclosed embodiments provide a system that facilitates geolocation of a user. The system includes a server and an electronic device. First, the server receives a location of the user from an electronic device of the user and determines a window around the location. Next, the server transmits the window and one or more geofences within the window to the electronic device. The electronic device then obtains one or more precision buffers associated with one or more levels of accuracy for detecting the location of the user near the one or more geofences. Finally, the electronic device facilitates use of the one or more geofences by varying a geolocation technique for tracking the location of the user based on the one or more precision buffers.

Abstract: The disclosed embodiments provide a system that dynamically updates a geofence of a mobile business. During operation, the system establishes a first location and a first geofence for the mobile business. Next, the system receives a second location of the mobile business different than the first location of the mobile business. The system then generates an updated geofence for the mobile business based on the second location of the mobile business. The system subsequently broadcasts the second location of the mobile business to a set of mobile clients monitoring the mobile business.