The present disclosure relates to a system and computer-implemented method for character recognition in a payment card. The method includes receiving an image of a payment card and one or more details associated with the payment card. Further, a derivative of the image is determined based on the one or more details and a horizontal sum of pixel values is determined for a plurality of rows in the image. Furthermore, one or more Regions of Interest (ROIs) are identified in the image by comparing the horizontal sum of pixel values with a predefined first threshold. Subsequently, one or more characters in the one or more ROIs are extracted using one or more peak values in a histogram of the one or more ROIs. Finally, each of the one or more characters extracted from the one or more ROIs is recognized using a trained Artificial Intelligence technique.

The present disclosure relates to a system and computer-implemented method for character recognition in a payment card. The method includes receiving an image of a payment card and one or more details associated with the payment card. Further, a derivative of the image is determined based on the one or more details and a horizontal sum of pixel values is determined for a plurality of rows in the image. Furthermore, one or more Regions of Interest (ROIs) are identified in the image by comparing the horizontal sum of pixel values with a predefined first threshold. Subsequently, one or more characters in the one or more ROIs are extracted using one or more peak values in a histogram of the one or more ROIs. Finally, each of the one or more characters extracted from the one or more ROIs is recognized using a trained Artificial Intelligence technique.

A memory device is provided including instructions that, when executed, cause one or more processors to perform the steps including receiving a plurality of images acquired by a camera, the plurality of images including a plurality of optical patterns, wherein an optical pattern of the plurality of optical patterns encodes an object identifier. The steps include presenting the plurality of images comprising the plurality of optical patterns on a display, and presenting a plurality of visual indications overlying the plurality of optical patterns in the plurality of images. The steps also include identifying a selected optical pattern of the plurality of optical patterns based on a user action and a position of the selected optical pattern in one or more of the plurality of images. The steps also include decoding the selected optical pattern to generate the object identifier and storing the object identifier in a second memory device.

A method of image analysis is provided for recognition of a pattern in an image. The method includes receiving a plurality of images acquired by a camera, where the plurality of images include a plurality of optical patterns in an arrangement. The method also includes matching the arrangement to a pattern template, wherein the pattern template is a predefined arrangement of optical patterns. The method also includes identifying an optical pattern of the plurality of optical patterns as a selected optical pattern based on a position of the selected optical pattern in the arrangement. The method also includes decoding the selected optical pattern to generate an object identifier and storing the object identifier in a memory device.

An image processing system according to the present embodiment acquires a processing target image read from an original that is handwritten and specifies one or more handwritten areas included in the acquired processing target image. In addition, for each specified handwritten area, the present image processing system extracts from the processing target image a handwritten character image and a handwritten area image indicating an approximate shape of a handwritten character. Furthermore, for a handwritten area including a plurality of lines of handwriting among the specified one or more handwritten areas, a line boundary of handwritten characters is determined from a frequency of pixels indicating a handwritten area in a line direction of the handwritten area image, and a corresponding handwritten area is separated into each line.

An image processing system according to the present embodiment acquires a processing target image read from an original that is handwritten and specifies one or more handwritten areas included in the acquired processing target image. In addition, for each specified handwritten area, the present image processing system extracts from the processing target image a handwritten character image and a handwritten area image indicating an approximate shape of a handwritten character. Furthermore, for a handwritten area including a plurality of lines of handwriting among the specified one or more handwritten areas, a line boundary of handwritten characters is determined from a frequency of pixels indicating a handwritten area in a line direction of the handwritten area image, and a corresponding handwritten area is separated into each line.

Potentially fraudulent documents can be identified automatically and flagged for further review. Document fraud detection can include a number of fraud detection tests, where if the document fails any of the tests, the document can be flagged for further review. Various tests can be performed that seek to determine whether a document has been altered. In one instance, a financial instrument can be scanned and analyzed to identify one or more layers associated with the financial instrument from a scanned representation, and a check of transaction details can be triggered when the financial instrument is associated with multiple layers.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium for predicting object pose. In one aspect, a method includes receiving an image of an object having one or more feature points; providing the image as an input to a neural network subsystem trained to receive images of objects and to generate an output including a heat map for each feature point; applying a differentiable transformation on each heat map to generate respective one or more feature coordinates for each feature point; providing the feature coordinates for each feature point as input to an object pose solver configured to compute a predicted object pose for the object, wherein the predicted object pose for the object specifies a position and an orientation of an object; and receiving, at the output of the object pose solver, a predicted object pose for the object in the image.

The subject matter of this specification can be implemented in, among other things, a method that includes identifying edges of a section of a document in a source image that includes at least one row of text. The method includes identifying characters in the document. The method includes identifying word portions. The method includes generating polynomials that approximate points of the characters within the word portions. The method includes generating a second polynomial that approximates the points of the characters of word portions. The method includes identifying a stretching coefficient of the row of text based on a length of the section between the edges relative to a length of the second polynomial. The method includes mapping portions of the source image along the row of text to new positions in a corrected image based on the second polynomial and the stretching coefficient.

In one aspect, a method of optical character recognition of digital character objects in digital images includes the step of obtaining a digital image. The digital images include rendering of a first object in the digital image. The first object comprises a set of sub-objects and a set of relationships between the sub-object. The method includes the step of generating a definition of a first object by defining an object outline for the first object as a set of sub-objects; defining a sub-object outline for each sub-object as a set of lines and curves; and defining each relationship between each set of connected sub-objects in terms of one or more intersections or one or more corners.