An example electronic apparatus includes a user interface device, a communication device, a processor, and a memory to store instructions executable by the processor. The processor is to execute the instructions to obtain an image regarding a document including handwritten text, distinguish a writer who writes the handwritten text based on feature information of the handwritten text that is read from the image, and generate a file regarding the document based on at least one of setting information of the writer or setting information of the handwritten text.

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 method for generating a target function is provided. The method includes: performing normalization processing on a vector corresponding to each pixel in a target feature map set to generate a target vector, so as to obtain a target vector set; generating hash coding corresponding to each vector in the target vector set, to obtain a hash coding set; determining a prior probability of each hash coding in the hash coding set; and generating a target function based on an entropy of the prior probability.

A character segmentation method and apparatus, and a computer-readable storage medium are provided. The method includes converting a character area image into a grayscale image; converting the grayscale image into an edge binary image by edge detection; acquiring character box segmentation blocks from the edge binary image by projection; and determining a target character area from the character box segmentation blocks by contour detection, and performing character segmentation on the character area image according to the target character area; or comprises: converting a character area image into a grayscale image; performing clustering analysis on the grayscale image by fuzzy C-means clustering, and binarizing the grayscale image according to the analysis result; acquiring character positioning blocks from a binary image by projection; and performing character segmentation on the character area image according to position information of the character positioning blocks. Character segmentation can be performed on a relatively low quality image.

Methods, systems, and apparatus are provided for detecting and extracting data from a nested table. A contour image of a first table disposed within a cell of a second table from an input image is identified. The first table on the input image is masked to generate a masked image of the second table that omits the first table. Cells of the first table and the second table are detected by identifying a plurality of regions of interest (ROIs), determining a row of a plurality of cells based on a common y-coordinate among of the plurality of ROIs, and generating cell parameter information for each of the plurality of cells that identifies the y-coordinate, a size, and a relative position of each determined cell. An output file is generated based on the cell parameter information of the plurality of cells.

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 method for generating a dashboard from an image, the method comprises: receiving an image of at least part of a first dashboard generated using a first dashboard platform; applying an object detection model to the image to detect a chart in the image; extracting a portion of the image comprising the chart; applying a classification model to the extracted portion of the image to classify the chart; applying an optical character recognition model to the extracted portion of the image to generate one or more text instances based on the extracted portion; labeling the one or more text instances; applying a matching algorithm based on the one or more labeled text instances to match to text strings in a database; and providing instructions for a second dashboard platform to generate a second dashboard based on data corresponding to the text strings in the database.

The image reading apparatus includes a reading section and a control section. The control section includes an image acquisition unit, a threshold setting unit, and a binarization processing unit. The threshold setting unit performs threshold setting processing including second processing and third processing, thereby setting a threshold used for binarization processing for each pixel of a read image. The second processing sets a threshold for binarizing a background pixel as a pixel of a background image into white. The third processing sets a threshold for binarizing a character pixel as a pixel of a character image into black. The binarization processing unit generates a binarized image from the read image based on the threshold set for each pixel. The threshold setting unit sets a threshold determined based on each brightness value of nn pixels containing a pixel as a pixel of interest for the pixel of interest.

The disclosure relates to systems and methods automatically extracting an image and related image components, computationally determining an understanding of the image, and generating mathematical vector embeddings via sentence encoders based on the computationally determined understanding. The mathematical vector embeddings may be used for semantic image retrieval that enables image searching based on a semantic understanding of input images and/or input text. The mathematical vector embeddings may be used for training and executing generative Artificial Intelligence (AI) models to create new content that includes retrieved images and/or generate new images.

Disclosed herein are designs for two baselines to detect products in a retail setting. A novel detector, referred to herein as RetailDet, detects quadrilateral products. To match products using visual texts on 2D space, text features are encoded with spatial positional encoding and the Hungarian Algorithm that calculates optimal assignment plans between varying text sequences is used.