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.

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.

Disclosed embodiments include a method for dynamically providing notary sessions. The method can include receiving a document and one or more user-level notary requirements from a user device. Data entries can be extracted from the document and the document can be associated with a template. Document-level notary requirements can be determined based on the template and document. A first subset of active notary devices can be identified, and a first join request can be transmitted to the first subset. A first notary session between a first notary device and first user device can be initiated if the join request is accepted within a predetermined time threshold. If the join request is not accepted within a predetermined time threshold, the method can include identifying a second subset of active notary devices, transmitting a second join request, and initiating a second notary session between the first user device and second notary device.

A system and methods for automatically delineating OARs in whole-volume medical images through a two-stage DCNN model, the DCNN model comprising an OAR detection network and an OAR segmentation network, and the method comprising the steps of: inputting the whole-volume medical images to the OAR detection network; extracting image features through a sequence of downsampling blocks; generating a final detection feature map via upsampling and concatenation; detecting at least one OAR candidate by branching the final detection feature map, wherein the at least one OAR candidate is defined by a predicted bounding box with a class label; inputting the predicted bounding box and corresponding class label to the OAR segmentation network; cropping the final detection feature map and a downsampling block in the OAR detection network according to the predicted bounding box; concatenating the cropped feature maps and generating a predicted binary mask delineating OARs according to the class label.

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.

A system and methods for automatically delineating OARs in whole-volume medical images through a two-stage DCNN model, the DCNN model comprising an OAR detection network and an OAR segmentation network, and the method comprising the steps of: inputting the whole-volume medical images to the OAR detection network; extracting image features through a sequence of downsampling blocks; generating a final detection feature map via upsampling and concatenation; detecting at least one OAR candidate by branching the final detection feature map, wherein the at least one OAR candidate is defined by a predicted bounding box with a class label; inputting the predicted bounding box and corresponding class label to the OAR segmentation network; cropping the final detection feature map and a downsampling block in the OAR detection network according to the predicted bounding box; concatenating the cropped feature maps and generating a predicted binary mask delineating OARs according to the class label.

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.

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.

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.