An image generation method obtains an original image. A character area, a background area, and a position of each flawless character in the original image are determined. The character area is segmented to obtain a first image of each flawless character. A background is removed from the first image to obtain a second image. First image processing is performed on the second image to obtain a third image. Second image processing is performed on the second image to obtain fourth images. Third image processing is performed on the fourth images respectively to obtain fifth images. A similarity between each fifth image and the third image is calculated. When the similarity is greater than a defect threshold, a background image is segmented. Brightness of the background image is adjusted. The target fourth image and adjusted background image are synthesized. The method can generate images with defective characters quickly.

Surgical kit inspection systems and methods are provided for inspecting surgical kits having parts of different types. The surgical kit inspection system comprises a vision unit including a first camera unit and a second camera unit to capture images of parts of a first type and a second type in each kit and to capture images of loose parts from each kit that are placed on a light surface. A robot supports the vision unit to move the first and second camera units relative to the parts in each surgical kit. One or more controllers obtain unique inspection instructions for each of the surgical kits to control inspection of each of the surgical kits and control movement of the robot and the vision unit accordingly to provide output indicating inspection results for each of the surgical kits.

Various embodiments disclosed herein are directed to methods of capturing Vehicle Identification Numbers (VIN) from images captured by a mobile device. Capturing VIN data can be useful in several applications, for example, insurance data capture applications. There are at least two types of images supported by this technology: (1) images of documents and (2) images of non-documents.

A computer implemented method of performing single pass optical character recognition (OCR) including at least one fully convolutional neural network (FCN) engine including at least one processor and at least one memory, the at least one memory including instructions that, when executed by the at least processor, cause the FCN engine to perform a plurality of steps. The steps include preprocessing an input image, extracting image features from the input image, determining at least one optical character recognition feature, building word boxes using the at least one optical character recognition feature, determining each character within each word box based on character predictions and transmitting for display each word box including its predicted corresponding characters.

The present disclosure is directed to systems and methods for strike through detection and, more particularly, to systems and methods for detecting a strike through in an address block of a mailpiece. The method is implemented in a computing device and includes: generating edges of lines within a text block identified through optical character recognition processes; locating text lines within the text block; characterizing the edges within the text lines and outside of the text lines; and grouping identified edges of the characterized edges outside of the text lines into co-linear groups.

An information processing apparatus includes a processor configured to receive an input image including images of plural documents, execute detection of one or more items determined in advance as an item included in the document from the input image, and execute output processing of extracting and outputting the image of each document from the input image based on the detected one or more items.

A data processing device and a computer-implemented method are configured to execute in parallel a data hub process (6) comprising at least a segmentation sub-process (61) which segments input data into data segments and at least one keying sub-process (62) which provides keys to the data segments creating keyed data segments, wherein the data hub process (6) stores the keyed data segments in a shared memory device (4) as shared keyed data segments and a plurality of processes in the form of computation modules (7) wherein each computation module (7) is configured to access the at least one shared memory device (4) to look for modulo-specific data segments which are shared keyed data segments that are keyed with at least one key which is specific for at least one of the computation modules (7) and to execute a machine learning method on the module-specific data segments, said machine learning method comprising data interpretation and classification methods using at least one pre-trained neuronal network (71) and to output the result of the executed machine learning method to the shared memory device (4) or another computation module.

A wine label recognition method, a wine information management method and apparatus, a computer device, and a computer-readable storage medium are provided. The method includes: obtaining a wine image, and performing optical character recognition (OCR) on the wine image in a preset OCR manner, to obtain text included in the wine image (S21); performing deep learning recognition on the wine image in a preset deep learning recognition manner, to obtain an image feature included in the wine image (S22); and sifting out a target wine label matching the text and the image feature from a preset wine label database according to the text and the image feature, and using the target wine label as a wine label corresponding to the wine image (S33). Advantages of deep learning and OCR are fully utilized thereby improving accuracy and efficiency of wine label recognition and improving automation efficiency of wine information management.

Methods, systems, and techniques for processing textual depictions in a computer game screenshot. At least one processor is used to obtain a screenshot of a computer-implemented game, wherein the screenshot includes a game data region. The game data region includes a textual depiction indicative of how a user performed in the game. The at least one processor segments the textual depiction from the screenshot by performing image segmentation on the screenshot and determines alphanumeric characters corresponding to the textual depiction. After determining the alphanumeric characters, the at least one processor outputs the characters to, for example, storage or a display. Image processing in the form of any one or more of aspect ratio transformation, cropping, scaling, binarizing, and blurring may be done prior to segmentation.

A method for assisting manufacturing outsourcing from a manufacturer to a processor of a component is disclosed. The method includes receiving, from a manufacturer terminal of the manufacturer, two-dimensional data expressing the component in a two-dimensional manner. The method includes extracting designation information from the two-dimensional data. The method includes determining outsourcing requirements corresponding to the designation information. The method includes adding, to the two-dimensional data, additional data corresponding to at least part of the outsourcing requirements. The method includes transmitting, to a processor terminal of the processor, the two-dimensional data to which the additional data is added.