A method for printing defect detection includes processing and analyzing a difference image obtained by comparing an image scanned with a verifier to a reference image. The detected defects are grouped, and the grouping is refined. Confidence level values are then assigned to the refined groups, and analysis is performed to determine if one or more servicing actions should be taken.

A system and method for toner classification from device printouts includes applying machine learning to electronic documents formed from scanned printout images. A training set is formed by scanning documents known to be printed with OEM toner, supplemented by scanning documents known to be printed with non-OEM toner. When new printouts are made, they are scanned and analyzed by an AI/ML server and classified as printed by OEM toner or by non-OEM toner.

An information processing method including: acquiring a superimposed image in which electronic information corresponding to an image of an object captured by an image capturing unit of an information processing apparatus is superimposed on the image; storing the superimposed image in a storage; and displaying the superimposed image stored in the storage on a display of the information processing apparatus, and even in a case where the object is not included in an image capturing range of the image capturing unit, the superimposed image stored in the storage is displayed on the display.

An image processing apparatus for converting color data into gray data extracts an object included in the color data in a page, and determines, based on color information and position information on a first object and a second object extracted, whether at least one of the first object and the second object is to be a target of conversion of a color value of the color data into a gray value with improved discriminability. The image processing apparatus controls to convert the color value of the object into a gray value by a conversion targeting all color values, or into a gray value with the improved discriminability, based on the determination whether or not to be the target of conversion.

An image forming apparatus includes a recording medium accommodation portion that accommodates a recording medium, a storage portion that stores information about the recording medium that is accommodated in the recording medium accommodation portion and previously printed, a sensing portion that senses opening and closing of the recording medium accommodation portion, a comparison portion that compares currently set setting information about the recording medium with the information about the recording medium stored in the storage portion on a condition that the sensing portion senses that the recording medium accommodation portion is not open between previous printing and current printing, and an output portion that outputs a comparison result of the comparison portion.

An information processing system configured to perform setting for an image processing apparatus based on setting edited by an application configured to operate on an information processing apparatus, wherein the system includes a startup unit that acquires a status of the image processing apparatus, downloads setting information for the image processing apparatus, determines whether or not to start up the application, and controls the startup of the application based on results of the determination, wherein the determination is performed based on the status before the setting information download has begun.

For each region of interest (ROI) type of a number of ROI types, ROIs of the ROI type within a reference image are compared to corresponding ROIs within a test image corresponding to the reference image and printed by a printing device. For each ROI type, a feature vector characterizing image quality defects within the test image for the ROI type is generated based on results of the comparing for the ROI type. Whether print quality of the printing device has degraded below a specified acceptable print quality level can be assessed based on the feature vectors for the ROI types.

An example of an apparatus including a communication interface to receive an image file is provided. The image file represents a scanned image of a output generated by a printing device. The apparatus further includes an identification engine to process the image file with a convolutional neural network model to identify a feature. The feature may be indicative of a potential failure. The apparatus also includes an image analysis engine to indicate a life expectancy of a part associated with the potential failure based on the feature. The image analysis engine uses the convolutional neural network model to determine life expectancy.

There is provided an image forming system including at least a first image forming apparatus that includes a first image forming unit and an obtaining unit configured to obtain operation-related information related to an operation of the first image forming unit. The image forming system further includes a carryover unit configured to carry over, to a second image forming apparatus, information related to an operation of a second image forming unit of the second image forming apparatus among the operation-related information obtained by the first image forming apparatus when the second image forming apparatus is newly used in the image forming system. The operation-related information includes operation history information of the first image forming unit.

A system and method for toner classification from device printouts includes applying machine learning to electronic documents formed from scanned printout images. A training set is formed by scanning documents known to be printed with OEM toner, supplemented by scanning documents known to be printed with non-OEM toner. When new printouts are made, they are scanned and analyzed by an AI/ML server and classified as printed by OEM toner or by non-OEM toner.