A processor may receive an image and determine a number of foreground pixels in the image. The processor may obtain a result of optical character recognition (OCR) processing performed on the image. The processor may identify at least one bounding box surrounding at least one portion of text in the result and overlay the at least one bounding box on the image to form a masked image. The processor may determine a number of foreground pixels in the masked image and a decrease in the number of foreground pixels in the masked image relative to the number of foreground pixels in the image. Based on the decrease, the processor may modify an aspect of the OCR processing for subsequent image processing.

An embodiment of the present invention is directed to a combination of two deep-learning computer vision models—customized with post-processing—wrapped in a mobile application that is backed by an Application Programming Interface (API) supporting concurrent mobile users to accomplish asset serialization tasks in a warehouse or other storage environment.

There are provided an image analysis apparatus, an image analysis method, and a program for implementing an image analysis method that can, when text information about a structural formula of a compound is generated from an image showing the structural formula, cope with a change in the way of drawing of the structural formula.

An image analysis apparatus according to one embodiment of the present invention includes a processor, and the processor is configured to generate, on the basis of a feature value of a subject image showing a structural formula of a subject compound, symbol information representing the structural formula of the subject compound with a line notation, by using an analysis model. The analysis model is a model created through machine learning using a learning image and symbol information representing a structural formula of a compound shown by the learning image with a line notation.

There are provided an image analysis apparatus, an image analysis method, and a program for implementing an image analysis method that can, when text information about a structural formula of a compound is generated from an image showing the structural formula, cope with a change in the way of drawing of the structural formula.

An image analysis apparatus according to one embodiment of the present invention includes a processor, and the processor is configured to generate, on the basis of a feature value of a subject image showing a structural formula of a subject compound, symbol information representing the structural formula of the subject compound with a line notation, by using an analysis model. The analysis model is a model created through machine learning using a learning image and symbol information representing a structural formula of a compound shown by the learning image with a line notation.

A delivery processing apparatus has a recognition unit and a determination unit. The recognition unit executes recognition processing for recognizing information that is provided on a delivery target item based on an image obtained by shooting an image of the delivery target item. The determination unit determines whether or not to extend the recognition processing performed by the recognition unit, based on a degree of progress of the recognition processing that has been performed by the recognition unit in a period from when the information recognition processing was started by the recognition unit until a predetermined time has elapsed, and an extension rate, which indicates a ratio of the number of times that the recognition unit has extended the recognition processing to the number of times that the recognition unit has performed the recognition processing.

This specification describes methods and systems for printed code inspection. For instance, the specification describes a computer-implemented method for printed code inspection by a printed code inspection system operating in conjunction with a production line apparatus configured to move objects along a production line comprising: receiving an image of an object to which a printed code comprising one or more printed characters should have been applied, the image having been captured when the object was located at a particular position on the production line; analysing the image to detect, based on a set of one or more character identification parameters, at least one candidate character within the image; determining, for each of the at least one candidate characters and based on a set of one or more candidate character properties, a likelihood that the candidate character is one of the printed characters of the printed code that should have been applied to the object; determining, based on the candidate characters determined as being likely to be one of the printed characters of the printed code that should have been applied to the object, whether the printed code is present and legible on the object; and outputting an indication as to whether the printed code that should have been applied to the object is present and legible on the object.

Disclosed herein are computer-implemented methods; computer-implemented systems; and non-transitory, computer-readable media, for quantifying text similarity. One computer-implemented method includes obtaining a plurality of shortest operation paths including one or more edit pairs for correcting an optical correction recognition (OCR) text string with an edit text string, where each of the one or more edit pairs denotes an operation performable to a character of the OCR text string during correction by the edit text string. A plurality of similarity scores is determined, each corresponding to one of the plurality of shortest operation paths and determined by summing historical similarity scores of the one or more edit pairs of each of the plurality of shortest operation paths. A minimum one of the plurality of similarity scores is selected to quantify text similarity between the OCR text string and the edit text string.

A device for digitizing papers, including a controller having a processor and memory, a digitizing scanner configured to accept a paper, digitize content of the paper and transfer the digitized content to the controller, a program executed by the controller that is configured to identify calibration markings or unique fingerprints from specific locations of the paper, embedded in the digitized content, wherein the program provides a determination that the digitizing scanner is functioning correctly if the calibration markings or unique fingerprint are identified as being correct or by identifying a type of paper digitized from characteristics of the unique fingerprint.

A processor determines a cause of an image defect based on a test image that is obtained through an image reading process performed on an output sheet output from an image forming device. The processor generates an extraction image by extracting, from the test image, a noise point that is a type of image defect. Furthermore, the processor determines a cause of the noise point by using at least one of, in the extraction image: an edge strength of the noise point; a degree of flatness of the noise point; and a pixel value distribution of a transverse pixel sequence that is a pixel sequence traversing the noise point.

A processor determines a cause of an image defect based on a test image that is obtained through an image reading process performed on an output sheet output from an image forming device. The processor extracts a vertical stripe part extending along a sub scanning direction in the test image. Furthermore, the processor determines which of predetermined two types of cause candidates is a cause of the vertical stripe part, based on a distribution of a pixel value sequence along a main scanning direction that crosses the sub scanning direction, in a target part including the vertical stripe part in the test image.