A system and method are provided for facilitating reading of medical images on a display. The method includes receiving a current medical image corresponding to a subject, and displaying the current medical image; performing image segmentation of the current medical image using a deep learning algorithm to identify a region of interest, and displaying an annotation on the current medical image indicating the region of interest; retrieving a previous medical image and a radiology report corresponding to a previous medical image of the subject; to extract relevant findings; performing NLP on the descriptive text to extract relevant findings, and displaying the relevant findings on the display with the current medical image; and retrieving an interactive checklist from a checklist database, including items for the user to consider when reviewing the current medical image.

Vehicles and other items often have corresponding documentation, such as registration cards, that includes a significant amount of informative textual information that can be used in identifying the item. Traditional OCR may be unsuccessful when dealing with non-cooperative images. Accordingly, features such as dewarping, text alignment, and line identification and removal may aid in OCR of non-cooperative images. Dewarping involves determining curvature of a document depicted in an image and processing the image to dewarp the image of the document to make it more accurately conform to the ideal of a cooperative image. Text alignment involves determining an actual alignment of depicted text, even when the depicted text is not aligned with depicted visual cues. Line identification and removal involves identifying portions of the image that depict lines and removing those lines prior to OCR processing of the image.

Vehicles and other items often have corresponding documentation, such as registration cards, that includes a significant amount of informative textual information that can be used in identifying the item. Traditional OCR may be unsuccessful when dealing with non-cooperative images. Accordingly, features such as dewarping, text alignment, and line identification and removal may aid in OCR of non-cooperative images. Dewarping involves determining curvature of a document depicted in an image and processing the image to dewarp the image of the document to make it more accurately conform to the ideal of a cooperative image. Text alignment involves determining an actual alignment of depicted text, even when the depicted text is not aligned with depicted visual cues. Line identification and removal involves identifying portions of the image that depict lines and removing those lines prior to OCR processing of the image.

The present disclosure discloses methods and systems for detecting tampering of an infrared (IR) security mark in a document. The method includes receiving the document including the IR security mark, wherein the IR security mark further includes one or more security texts and/or images. The document is scanned to generate scanned data. A portion of the scanned data including the IR security mark is segmented into a plurality of blocks such as blocks of size 32*32. Thereafter, a ratio of white to black pixels is calculated for each block. The calculated ratio is compared with a known threshold for each block. Upon comparison, the IR security mark is detected as a tampered security mark.

A system for computerized authentication of a laminated object, the system comprising a digital medium storing a digital image of at least a portion of the laminated object in a computer-implemented memory; a shininess analyzer operative, using a processor, to generate shininess data quantifying shininess of the digital image; and a parameterized computerized authentication sub-system operative to differentially perform at least one laminated object authentication operation based on the shininess data.

In some embodiments, a method can include generating a resume document image having a standardized format, based on a resume document having a set of paragraphs. The method can further include executing a statistical model to generate an annotated resume document image from the resume document image. The annotated resume document image can indicate a bounding box and a paragraph type, for a paragraph from a set of paragraphs of the annotated resume document image. The method can further include identifying a block of text in the resume document corresponding to the paragraph of the annotated resume document image. The method can further include extracting the block of text from the resume document and associating the paragraph type to the block of text.

A system for computerized authentication of a laminated object, the system comprising a digital medium storing a digital image of at least a portion of the laminated object in a computer-implemented memory; a shininess analyzer operative, using a processor, to generate shininess data quantifying shininess of the digital image; and a parameterized computerized authentication sub-system operative to differentially perform at least one laminated object authentication operation based on the shininess data.

Vehicles and other items often have corresponding documentation, such as registration cards, that includes a significant amount of informative textual information that can be used in identifying the item. Traditional OCR may be unsuccessful when dealing with non-cooperative images. Accordingly, features such as dewarping, text alignment, and line identification and removal may aid in OCR of non-cooperative images. Dewarping involves determining curvature of a document depicted in an image and processing the image to dewarp the image of the document to make it more accurately conform to the ideal of a cooperative image. Text alignment involves determining an actual alignment of depicted text, even when the depicted text is not aligned with depicted visual cues. Line identification and removal involves identifying portions of the image that depict lines and removing those lines prior to OCR processing of the image.

In some embodiments, a method can include generating a resume document image having a standardized format, based on a resume document having a set of paragraphs. The method can further include executing a statistical model to generate an annotated resume document image from the resume document image. The annotated resume document image can indicate a bounding box and a paragraph type, for a paragraph from a set of paragraphs of the annotated resume document image. The method can further include identifying a block of text in the resume document corresponding to the paragraph of the annotated resume document image. The method can further include extracting the block of text from the resume document and associating the paragraph type to the block of text.

A system and method of for describing target data as a sequence of pattern elements and pattern element groups that comprise an overall target pattern is described. Pattern elements may utilize regular expression syntax along with other metadata that describe the behavior of the element. A pattern element group may be a collection of fully defined pattern elements where at least one pattern element from the group must have a match for the overall pattern to match. Patterns contain both pattern elements and pattern element groups. The general process involves first performing optical character recognition (OCR) on the document, which in turn produces a sequence of text tokens representing the lines of text on each page of the document. The search algorithm may then apply each defined pattern to the entire document capturing and/or extracting data that match each pattern's required elements and element groups.