A computer-implemented method for vehicle registration number recognition, the method comprising obtaining a vehicle registration number to be recognised and a plurality of authorised vehicle registration numbers, and comparing the vehicle registration number to be recognised with the plurality of authorised vehicle registration numbers, wherein comparing comprises: identifying the number of characters in common between the vehicle registration number to be recognised and each of the plurality of authorised vehicle registration numbers; determining whether the number of characters in common between the vehicle registration number to be recognised and each authorised vehicle registration number is above a pre-determined threshold but less than all characters; and in the event that the number of characters in common between the vehicle registration number to be recognised and at least one authorised vehicle registration number is above the pre-determined threshold, sending a signal that the vehicle registration number is recognised.

The present disclosure proposes a method for recognizing a license plate number on an image, comprising the following steps: detecting a license plate on a vehicle in an image; recognizing characters on the license plate and coordinates of the characters by a neural network, wherein the loss function of the neural network consists of classification loss, confidence loss and Complete Intersection over Union (CIOU) loss; and organizing the recognized characters based on the coordinates to form the recognized license plate number. By this method, when recognition of a license plate number on an image is performed, the network bandwidth and the hardware cost may be greatly reduced.

A system and method for correcting image data is disclosed. The method includes receiving one or more documents from one or more electronic mediums. The method further includes determining a primary character and one or more alternate characters corresponding to the mis-captured character image, extracting one or more confident instances of the primary character and the one or more alternate characters from the one or more documents and generating one or more scores corresponding to the primary character and the one or more alternate characters. Further, the method includes predicting a correct character corresponding to the mis-captured character image by using a trained image prediction-based ML model and automatically replacing the mis-captured character image with the predicted correct character.

A support apparatus includes a generation apparatus and an analysis apparatus. The generation apparatus executes (a-1) to (a-5) with I=1 to n, and generates pieces of process information. The generation apparatus extracts material words from a document i in (a-1), extracts a treatment word i from the document i in (a-2), extracts a synthesis condition i from the document i in (a-3), extracts a characteristic value i related to a target material from the document i in (a-4), and associates the material words, the treatment word i, the synthesis condition i, and the characteristic value i with each other to generate process information i in (a-5). The analysis apparatus includes a combiner that generates composite process information including a common part common to the pieces of process information and different parts different among the pieces of process information, and an outputter that outputs the composite process information.

A medicine image recognition method applied to an electronic device is provided. The method includes obtaining target images by inputting medicine images into a position detection network. Character feature matrices are generated according to the target images and a character recognition network. Image feature matrices are generated by inputting the target images into a category recognition network. Reference matrices are generated according to the image feature matrices and corresponding character feature matrices. Once a matrix to be tested is generated by processing an image to be tested, and a recognition result of the image to be tested is generated according to a similarity between the matrix to be tested and each of the reference matrices.

Keypoint extraction is done for extracting keypoints from images of documents. Based on different keypoint extraction approaches used by existing keypoint extraction mechanisms, number of keypoints extracted and related parameters vary. Disclosed herein is a method and system for keypoint extraction from images of one or more documents. In this method, a reference image and a test image of a document are collected as input. During the keypoint extraction, based on types of characters present in words extracted from the document images, a plurality of words are extracted. Further, all connected components in each of the extracted words are identified. Further, it is decided whether keypoints are to be searched in a first component or in a last component of all the identified connected components, and accordingly searches and extracts at least four of the keypoints from the test image and the corresponding four keypoints from the reference image.

A handwriting data generation apparatus includes a memory containing processor-executable instructions and a processor coupled to the memory. The processor is configured to perform, when loaded with the processor-executable instructions, associating tactile feedback with at least part of stroke data generated according to handwriting input, and generating digital ink including haptics data indicating the stroke data and the tactile feedback.

An information processing device includes an acquisition unit that acquires an image including a first symbol and a plurality of character strings and a relationship table indicating a correspondence relationship between a symbol and a character string, an extraction unit that extracts the first symbol and the plurality of character strings based on the image, a distance calculation unit that calculates distances between the first symbol and the plurality of character strings based on a position of the first symbol and positions of the plurality of character strings, and a determination unit that determines a character string having a relationship with the first symbol based on the relationship table and the distances.

Disclosed herein is a computing system. The computing system includes a memory and a processor. The memory stores processor executable instructions for a workflow recommendation assistant engine. The processor is coupled to the memory. The processor executes the workflow recommendation assistant engine to cause the computing device to analyze images of a user interface corresponding to user activity, execute a pattern matching of the images with respect to existing automations, and provide a prompt indicating that an existing automation matches the user activity.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for event ticket exchange. A method includes actions of: receiving ticket information for tickets that permit access to an event occurring at a venue, the ticket information indicating: a particular section of the venue to which the ticket permits access; and whether the ticket is available for exchange; generating rendering data that presents a graphical user interface (GUI) showing a map of the venue; receiving data representing a first input indicating selection of one or more sections of the venue; in response, generating updated rendering data that presents an updated GUI showing: the map of the venue; and a list of exchange options each including at least one ticket that is available for exchange; receiving data representing a second input indicating a selection of a first exchange option; and in response, executing the first exchange option.