A document authenticity identification method is provided. A dynamic anti-counterfeiting point is detected in each document image of a subset of a plurality of document images. A static anti-counterfeiting point is detected in a document image of the plurality of document images. A static anti-counterfeiting point feature is generated based on image feature information of the static anti-counterfeiting point that is extracted from the document image. A dynamic anti-counterfeiting point feature is generated based on image feature information of the dynamic anti-counterfeiting point and variation feature information of the dynamic anti-counterfeiting point. A first authenticity result corresponding to the static anti-counterfeiting point is determined based on the static anti-counterfeiting point feature. A second authenticity result corresponding to the dynamic anti-counterfeiting point is determined based on the dynamic anti-counterfeiting point feature. Authenticity of the document is determined based on the first authenticity result and the second authenticity result.

A method, apparatus, and non-transitory computer-readable storage medium for altering a digital image for a printing job, the method comprising receiving a requested printing job including the digital image, performing a segmentation on the digital image, extracting values of properties for a segment of the segmented digital image, determining, based on the extracted values of the properties for the segment, whether the segment of the digital image should be altered, and altering the segment of the digital image when it is determined the segment of the digital image should be altered, a resulting altered digital image being transmitted to a printer for printing.

Aspects of the present disclosure relate to object-based image capture. Embodiments include identifying a reference point corresponding to an object in an image of a series of images. Embodiments include comparing a position of the reference point in the image to positions of one or more corresponding reference points in one or more previous images in the series of images. Embodiments include determining a total number of images in the series of images. Embodiments include selecting, based on the comparing and the total number of images in the series of images, between: capturing the image; or declining to capture the image.

A CNN of an image forming apparatus includes: an encoder which compresses, for each tile image obtained by dividing an image into specific size pieces, information of the tile image; a decoder which restores the information of the tile image compressed by the encoder; and a blank sheet determination portion which determines whether the tile image corresponds to a blank sheet image. A segmentation image generation portion uses, when the blank sheet determination portion determines the tile image as being the blank sheet image, the blank sheet image for an image of a part corresponding to the tile image in a segmentation image, and uses, when the blank sheet determination portion determines the tile image as not being the blank sheet image, an output image of the decoder for an image of a part corresponding to the tile image in the segmentation image.

A method, apparatus, and non-transitory computer-readable storage medium for altering a digital image for a printing job, the method comprising receiving a requested printing job including the digital image, performing a segmentation on the digital image, extracting values of properties for a segment of the segmented digital image, determining, based on the extracted values of the properties for the segment, whether the segment of the digital image should be altered, and altering the segment of the digital image when it is determined the segment of the digital image should be altered, a resulting altered digital image being transmitted to a printer for printing.

A computer-implemented method of converting map data. The method includes: obtaining unstructured map data according to a first data representation, the unstructured map data representing or including a number of geometric entities where the first data representation is a technical drawing representation or a CAD data representation, and converting the unstructured map data according to the first data representation to structured map data according to a second data representation, where the second data representation is a graph data representation.

A computing system receives, from a client device, an image of a content item uploaded by a user of the client devices. The computing system divides the image into one or more overlapping patches. The computing system identifies, via a first machine learning model, one or more distortions present in the image based on the image and the one or more overlapping patches. The computing system determines that the image meets a threshold level of quality. Responsive to the determining, the computing system corrects, via a second machine learning model, the one or more distortions present in the image based on the image and the one or more overlapping patches. Each patch of the one or more overlapping patches are corrected. The computing system reconstructs the image of the content item based on the one or more corrected overlapping patches.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for implementing content-aware filters to autonomously remove scan marks from digital documents. In particular implementations, the disclosed systems utilize a set of targeted scan mark models in a scan mark removal pipeline. For example, each scan mark model includes a corresponding content-aware filter configured to identify document regions that match a designated class of scan marks to filter. Examples of scan mark models include staple scan mark models, punch hole scan mark models, and page turn scan mark models. In certain embodiments, the disclosed systems then use the scan mark models to generate mark-specific masks based on document input features. Additionally, in some embodiments, the disclosed systems combine the mark-specific masks into a final segmentation mask and apply the final segmentation mask to the digital document for correcting the identified regions with scan marks.

ROI (Region of Interest) detection is an important step in extracting relevant information from a document image. Such images are very high-resolution images in nature and size of images is in order of megabytes, which makes text detection pipeline very slow. Traditional methods detect and extract ROI from images, but these work only for specific image types. Other approaches include deep learning (DL) based methods for ROI detect which need intensive training and require high end computing infrastructure/resources with graphical processing unit (GPU) capabilities. Systems and methods of the present disclosure perform ROI extraction by partitioning input image into parts based on its visual perception and then classify the image in first or second category. Region of interest is extracted from a resized image based on the classification by applying image processing techniques. Further, the system determines whether the input image is a pre-cropped image or a normal scanned image.

Apparatuses, systems, methods, and computer program products are presented for a propensity module based optimization. An apparatus comprises a processor and a memory that stores code executable by the processor to receive an electronic submission for a pass/fail interface, identify information from the electronic submission to suggest to a user for entering into an input field for the pass/fail interface prior to submitting the electronic submission to the pass/fail interface to reduce a likelihood that the electronic submission will be rejected at the pass/fail interface, determine the likelihood that the electronic submission will be accepted by the pass/fail interface, and submit the electronic submission to the pass/fail interface in response to the likelihood satisfying a threshold.