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 image processing method and an electronic device are provided. The image processing method includes: receiving a first input performed by a user; identifying a first text region on a target image in response to the first input; training a text image of the first text region on the target image to obtain a first font style model of the first text region; receiving a second input performed by the user; in response to the second input, obtaining a first input text, and training the first input text according to the first font style model to obtain a second text matching a font style of the first text region; and replacing text of the first text region with the second text on the target image.

A method and system is provided for determining the denomination and related data for a currency item using a personal computing device, such as a mobile phone. The device includes or is connected to an image capture device that is preferably a digital video camera. At least one image of a target currency item is captured then processed for image quality. A further processing of the image includes a coordinate mapping. A comparison is made between individual pixels of the processed image based on the assigned coordinate mapping with a database of reference currency images to determine the currency denomination. Additional processing of the currency image provides the date and other data regarding the target currency item. A market value for the target currency item is identified by reference to a valuation database using the data determined for the currency item.

State of art techniques that utilize spatial association based Table structure Recognition (TSR) have limitation in selecting minimal but most informative word pairs to generate digital table representation. Embodiments herein provide a method and system for TSR from an table image via deep spatial association of words using optimal number of word pairs, analyzed by a single classifier to determine word association. The optimal number of word pairs are identified by utilizing immediate left neighbors and immediate top neighbors approach followed redundant word pair elimination, thus enabling accurate capture of structural feature of even complex table images via minimal word pairs. The reduced number of word pairs in combination with the single classifier trained to determine the word associations into classes comprising as same cell, same row, same column and unrelated, provides TSR pipeline with reduced computational complexity, consuming less resources still generating more accurate digital representation of complex tables.

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.

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.

A method and system is provided for determining the denomination and related data for a currency item using a personal computing device, such as a mobile phone. The device includes or is connected to an image capture device that is preferably a digital video camera. At least one image of a target currency item is captured then processed for image quality. A further processing of the image includes a coordinate mapping. A comparison is made between individual pixels of the processed image based on the assigned coordinate mapping with a database of reference currency images to determine the currency denomination. Additional processing of the currency image provides the date and other data regarding the target currency item. A market value for the target currency item is identified by reference to a valuation database using the data determined for the currency item.

A serial number recognition parameter determination apparatus includes: a generation unit, an identification unit, and an evaluation index calculation unit. The generation unit generates a parameter set of a program, the program being used when a paper sheet handing apparatus identifies, from an image of a paper sheet, character present regions in which characters that form a serial number are present. The identification unit identifies, from an image of the paper sheet, the character present regions by using the parameter set that is generated by the generation unit. The evaluation index calculation unit calculates an evaluation index of the parameter set based on the character present regions that are identified by the identification unit.