The present disclosure relates to a system and method for determining a like grade and value or range of values for collectible cards. The system is configured to perform a method comprising: providing a graded card database comprising identifying attributes, physical characteristics, and grade information of each of graded cards, wherein the grade information comprises grade and corresponding grading entity of the graded cards; receiving at least one image of an object card; determining identifying attributes and physical characteristics of the object card based on the at least one image; selecting, from the graded card database, a comparison group including potentially a plurality of comparison cards based on the identifying attribute of the object card; determining a similarity between the object card and each comparison card based on the physical characteristic; and determining a likely grade for the object card based on the similarity. A card value database with information of traded card may be additionally provided and a likely value or range of values may be determined.

Systems, computer program products, and techniques for detecting and/or reconstructing objects depicted in digital image data within a three-dimensional space are disclosed. The concepts utilize internal features for detection and reconstruction, avoiding reliance on information derived from location of edges. The inventive concepts provide an improvement over conventional techniques since objects may be detected and/or reconstructed even when edges are obscured or not depicted in the digital image data. In one aspect, detecting a document depicted in a digital image includes: detecting a plurality of identifying features of the document, wherein the plurality of identifying features are located internally with respect to the object; projecting a location of one or more edges of the document based at least in part on the plurality of identifying features; and outputting the projected location of the one or more edges of the document to a display of a computer, and/or a memory.

The present disclosure involves systems, software, and computer implemented methods for automatically identifying table locations and table cell types of located tables. One example method includes receiving a request to detect tables. Features are extracted from an input spreadsheet and provided to a trained table detection model trained to predict whether worksheet cells are table cells or background cells and to a cell classification model that is trained to classify worksheet cells by cell structure type. The table detection model generates binary classifications that indicate whether cells are table cells or background cells. A contour detection process is performed on the binary classifications to generate table location information that describes at least one table boundary in the spreadsheet. The trained cell classification model generates a cell structure type classification for each cell that is included in a table boundary generated by the contour detection process.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing visual constraint guides to automatically transform digital design objects within a digital document based on transformation of intersecting visual constraint guides. In particular, in one or more embodiments, the disclosed systems generate visual constraint guides and identifies digital design objects intersecting the visual constraint guides. Further, the disclosed systems can receive user input transforming the visual constraint guide. In response, the disclosed systems can transform both the visual constraint guide and associated digital design objects based on the received transformation. More specifically, the design guide system can transform the digital design objects relative to the visual constraint guide while maintaining distribution and alignment constraints of the digital design objects relative to the visual constraint guide.

Methods and systems for incorporating physical documents into a document review workflow involving electronic documents. One or more embodiments detect a presence of a physical document within a field of view of an AR device and map the physical document to an existing electronic document based on visual features of the physical document. Additionally, one or more embodiments determine at least one difference between the physical document and the electronic document and create, for the physical document and the electronic document a shared state mapping including the difference(s). One or more embodiments then apply the difference to the physical document or the electronic document by displaying the difference(s) in an AR layer within the field of view of the AR device or storing the difference(s) in the electronic document.

An inspection apparatus obtains a scanned image by reading a printed product, calculates a weighting factor of a weighting filter based on a difference between a value of a pixel of interest in a reference image used in creation of the printed product and values of peripheral pixels of the pixel of interest, performs filter processing by using the weighting filter having the weighting factors, with respect to a pixel of interest in the scanned image, which corresponds to the pixel of interest in the reference image, thereby shifting the pixel of interest in the scanned image, and create an inspection target image by calculating the weighting factors and performing the filter processing with respect to the shifted pixel of interest, and inspects quality of the printed product by collating the inspection target image with the reference image.

Methods and systems are presented for detecting a boundary of a document within a digital image. Upon receiving an image, the image is converted into a binary image. One or more kernel-based transformations are performed on the binary image using a horizontal kernel and a vertical kernel. A plurality of edges are identified based on the one or more kernel-based transformations. The plurality of edges includes a plurality of horizontal edges and a plurality of vertical edges. Multiple quadrilaterals are constructed using different combinations of horizontal edges and vertical edges from the plurality of edges. A particular quadrilateral is selected from the multiple quadrilaterals based on how well the edges fit the perimeters of the quadrilaterals. The selected quadrilateral is used to define a boundary of the document within the digital image.

A method and system are provided for removing noise from document images using a neural network-based machine learning model. A dataset of original document images is used as an input source of images. Random noise is added to the original document images to generate noisy images, which are provided to a neural network-based denoising system that generates denoised images. Denoised images and original document images are evaluated by a neural network-based discriminator system, which generates a predictive output relating to authenticity of evaluated denoised images. Feedback is provided backpropagation updates to train both the denoising and discriminator systems. Training sequences are iteratively performed to provide the backpropagation updates, such that the denoising system is trained to generate denoised images that can pass as original document images while the discriminator system is trained to improve the accuracy in predicting the authenticity of the images presented.

An inspection apparatus includes at least one memory that stores instructions, and at least one processor that executes the instructions to perform determining, in a case where an image of an inspection area is a blank and collation inspection is to be performed on the image of the inspection, whether correct data in the collation inspection is a blank, outputting an inspection result for the inspection area as a success, in a case where it is determined that the correct data is a blank, and outputting an inspection result for the inspection area as a failure, in a case where it is determined that the correct data is not a blank.

The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes training at least one machine learning model to determine features that can be used to determine whether an image is an authentic image of a document or an automatically generated document image, using a training set of authentic images and a training set of automatically generated document images. A request to classify an image as either an authentic image of a document or an automatically generated document image is received. The machine learning model(s) are used to classify the image as either an authentic image of a document or an automatically generated document image, based on features included in the image that are identified by the machine learning model(s). A classification of the image is provided. The machine learning model(s) are updated based on the image and the classification of the image.