Systems and methods are provided for generating a resource transmission request to initiate a workflow associated with resource transmission. In particular, the disclosed technology is directed to processing image data corresponding to a physical notice of a request for resource transmission to generate an electronic resource transmission request. The system captures image data of the notice and extracts data from the image data. As an example, the system matches the extracted data against predetermined forms and determines whether the notice is in a known format. In instances where there is no match (such that the notice has an unknown format), the system uses one or more of heatmaps, rules of locating field data, and/or a field data extraction model to assign respective field names with data values in the extracted data. The heatmap includes regions in the image data with a likelihood of data values corresponding to particular field names.

In various embodiments, methods and systems for implementing a media management system, for video data processing and adaptation data generation, are provided. At a high level, a video data processing engine relies on different types of video data properties and additional auxiliary data resources to perform video optical character recognition operations for recognizing characters in video data. In operation, video data is accessed to identify recognized characters. A video OCR operation to perform on the video data for character recognition is determined from video character processing and video auxiliary data processing. Video auxiliary data processing includes processing an auxiliary reference object; the auxiliary reference object is an indirect reference object that is a derived input element used as a factor in determining the recognized characters. The video data is processed based on the video OCR operation and based on processing the video data, at least one recognized character is communicated.

A computer-implemented method for obfuscating sensitive information associated with mail delivery is disclosed. The computer-implemented method includes identifying that a piece of mail directed towards a potential recipient includes a particular type of sensitive information. The computer-implemented method further includes selecting a mail obfuscation policy for the particular type of sensitive information based on the particular type of sensitive information. The computer-implemented method further includes performing an obfuscation action with respect to the particular type of sensitive information based on the selected mail obfuscation policy.

In some implementations, a device may receive an image of a document, the image depicting a reference feature associated with the document, the reference feature including at least one of: a face of a person, a machine-readable code, or a text field. The device may identify a rotational angle of the reference feature as depicted in the image based on comparing the reference feature as depicted in the image to one or more orientation parameters of the reference feature associated with a display orientation associated with the document. The device may rotate the image of the document by an angle to obtain an orientated image of the document, the angle being based on the rotational angle of the reference feature as depicted in the image. The device may provide the orientated image of the document for display.

A system and method for constructing a training dataset and training a neural network include obtaining a searchable portable document format (PDF) document, identifying a bounding box defining a region in a background image that is associated with an overlaying text object defined in the PDF document, determining an image crop of the PDF document according to the bounding box, and generating a training data sample for the training dataset, the training data sample comprising a data pair of the image crop and the associated text object.

Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives an image of a document. The program further detects a plurality of text based on the image of the document. The program also uses a machine learning model to predict whether each text in the plurality of text is one of a plurality of defined types of text. Based on the predicted types of text for the plurality of text, the program further determines a set of defined objects.

Aspects of the disclosure provide for automatically generating labels for sensor data. For instance, first sensor data for a vehicle may be identified. This first sensor data may have been captured by a first sensor of the vehicle at a first location during a first point in time and may be associated with a first label for an object. Second sensor data for the vehicle may be identified. The second sensor data may have been captured by a second sensor of the vehicle at a second location at a second point in time outside of the first point in time. The second location is different from the first location. A determination may be made as to whether the object is a static object. Based on the determination that the object is a static object, the first label may be used to automatically generate a second label for the second sensor data.

An imaging part obtains an image of a read object, and a character string recognizing part recognizes a character string in the image. An output format setting part sets one or more output formats of a character string to be read from the image and to be output. A character extracting part obtains a character string for output, at a portion matching any of the one or more output formats among the recognized character string. At this time, a notifying part notifies a possibility of misreading in a case where the character string for output having characters less than a notification threshold number is obtained.

An imaging part obtains an image of a read object, and a character string recognizing part recognizes a character string in the image. An output format setting part sets an output format of a character string to be read from the image and to be output. A character extracting part obtains a candidate for a character string for output, at a portion matching the output format among the recognized character string, and if plural candidates are obtained, selects one of the plural candidates as the character string for output, based on a predetermined condition. A notifying part notifies a possibility of misreading in a case where plural candidates are obtained by the character extracting part.

An imaging part obtains an image of a read object, a shape recognizing part recognizes shapes in the image, and a character string recognizing part recognizes a character string among the recognized shapes. A notifying part notifies a possibility of misreading in a case where a shape, among the recognized shapes, not constituting any character exists in or near the recognized character string. When a character extracting part obtains a character string for output, that is all or a part of the recognized character string, the notification of the possibility of misreading may be performed also in a case where a shape not constituting any character exists in or near the obtained character string for output.