An inspection apparatus operable to inspect printed matter is provided. The inspection apparatus, in a case where an inspection process for inspecting a defect of the printed matter is set, performs an inspection process on the scanned image data to be inspected, which has been obtained by reading the printed matter, based on a reference image, and in a case where a character quality inspection process for inspecting a quality of a character of the printed matter is set, performs a character quality inspection process on the scanned image data to be inspected based on the reference image. The inspection apparatus makes an instruction so as to adjust image data to be printed by a printing apparatus that generated the printed matter in accordance with an inspection result of the character quality inspection process.

A viewing assistance system includes a display unit, a captured image generation unit, a voice recognition unit, and an association storage unit. The display unit includes a display screen on which a content having multiple pages are displayable. The captured image generation unit that generates a captured image of the content that is displayed on the display screen. The voice recognition unit that recognizes a voice included in the content. The association storage unit that associates a voice recognition result that is a result of recognizing the voice included in the content by the voice recognition unit with the captured image generated by the captured image generation unit to store.

A delivery system may include a pallet wrapper system having a turntable, a camera directed toward an area above the turntable, and a stretch wrap dispenser adjacent the turntable. A computer receives images from the camera of multiple sides of a pallet loaded with packages on the turntable. The computer stitches images from different sides of the stack of packages that correspond to the same package. At least one machine learning model may be used to infer SKUs of each package. Optical character recognition may be performed in parallel on the images. The determination of the SKU of each package may be based upon the inferred SKUs and on the OCR.

A system and method for efficient and secure private similarity detection for large private document repositories reduces the amount of trust that the participants need to give to a third party and detects malicious participants. One use of this system and method is the efficient and secure detection of similar documents across large private document repositories.

A device, method, and non-transitory computer readable medium are described. The method includes receiving a dataset including hand written Arabic words and hand written Arabic alphabets from one or more users. The method further includes removing whitespace around alphabets in the hand written Arabic words and the hand written Arabic alphabets in the dataset. The method further includes splitting the dataset into a training set, a validation set, and a test set. The method further includes classifying one or more user datasets from the training set, the validation set, and the test set. The method further includes identifying the target user from the one or more user datasets. The identification of the target user includes a verification accuracy of the hand written Arabic words being larger than a verification accuracy threshold value.

A delivery system may include a pallet wrapper system having a turntable, a camera directed toward an area above the turntable, and a stretch wrap dispenser adjacent the turntable. A computer receives images from the camera of multiple sides of a pallet loaded with packages on the turntable. The computer stitches images from different sides of the stack of packages that correspond to the same package. At least one machine learning model may be used to infer SKUs of each package. Optical character recognition may be performed in parallel on the images. The determination of the SKU of each package may be based upon the inferred SKUs and on the OCR.

A non-transitory computer readable medium (107, 127) stores instructions executable by at least one electronic processor (101, 113) to perform a component co-replacement recommendation method (200). The method includes: identifying components of a medical device by analyzing a technical document (130) related to the medical device; identifying component symbols (132) representing the components in drawings of the technical document; extracting relationships between the components of the medical device based on graphical connections (136) between the component symbols in the drawings of the technical document; generating a component connections graph (124) representing the relationships between the components of the medical device, the graph including nodes (138) corresponding to the components and connections (136) between the components; receiving an identification of a component to be replaced; and determining a co-replacement recommendation (122) for the component to be replaced based on the component connections graph.

Systems and methods relating to multitask transfer learning. Neural networks are used to accomplish a number of tasks and the results of these tasks are used to determine parameters common to these and other tasks. These parameters can then be used to accomplish other related tasks. In the description, data fitting as well as image related tasks are used. Task conditioning as well as the use of a KL regularizer have greatly improved results when testing the methods of the invention.

A method and system for processing images for a search is provided, including: receiving a plurality of images selected from search results; for each image in the plurality of images, retrieving a feature vector associated with the image; selecting a subset of the feature vectors based on similarity of feature vectors associated with the images in the plurality of images; and performing a search for feature vectors in a database similar to the feature vectors in the subset of feature vectors.

Improved techniques to access content from documents in an automated fashion. The improved techniques permit extraction of data from documents, namely, images of documents. The extraction processing can be hierarchical, such as being performed in multiple levels (i.e., multi-leveled). At an upper level, numerous different objects within a document can be detected along with positional data for the objects and can be categorized based on a type of object. Then, at lower levels, the different objects can be processed differently depending on the type of object. As a result, data extraction from the document can be performed with greater reliability and precision.