This disclosure relates to system and method for optical character recognition. In one embodiment, the method comprises providing an image data to a plurality of customized machine learning algorithms or various customized neural networks, configured to recognize a set of pre-defined characters. The method comprises presenting one or more suggestions for the character to the user in response to negative character recognition, and training a customized machine learning algorithm corresponding to the character if one of the suggestions is identified by the user. If the suggestions are rejected by the user, the method comprises prompting the user to identify the character and determining presence of the character in the set of pre-defined characters. The method further comprises training a customized machine learning algorithm corresponding to the character if the character is present, or dynamically creating a customized machine learning algorithm corresponding to the character if the character is not present.

An image processing device including an object detection unit for detecting one or more images of objects from an input picture image, on the basis of a model pattern of the object, and a detection result display unit for graphically superimposing and displaying a detection result. The detection result display unit includes a first frame for displaying the entire input picture image and a second frame for listing and displaying one or more partial picture images each including an image detected. In the input picture image displayed in the first frame, a detection result is superimposed and displayed on all the detected images, and in the partial picture image displayed in the second frame, a detection result of an image corresponding to each partial picture image is superimposed and displayed.

Techniques are described to generate improved training data for pixel labeling. To generate training data, objects are displayed in a user interface by a computing device, e.g., iteratively. The objects are taken from a structured object representation associated with a respective one of a plurality of images. The structured object representation defines a hierarchical relationship of the objects within the respective image. Inputs are then received that are originated through user interaction with the user interface. The inputs label respective ones of the iteratively displayed objects, e.g., as text, a graphical element, background, foreground, and so forth. A model is trained by the computing device using machine learning.

A user may markup the training documents to identify salient terms in a set of training unstructured documents. The system may automatically generate an extraction ruleset for each salient term that can be manually modified or edited by the user. The user may also provide analysis rulesets for each of the salient terms using, for example, a no-code graphical user interface. A machine learning model can be trained to automatically extract and analyze the salient terms based on feature vectors built from the extraction rulesets and/or analysis rulesets of the salient terms. After training, the system may import a set of unstructured documents for term extraction and analysis by the trained machine learning model. The system may generate a report, such as a PDF or an interactive graphical user interface, summarizing the results of the extracted and analyzed salient terms.

Computer-implemented systems and methods enhance a user's sophistication as she/he reviews complex information sources using specialized detective tools provided by a user interface of the computer system. The specialized investigative inquiries are stored in a database and are particularly tailored a priori by a subject-matter content designer for the type of documents being reviewed for risk and opportunity. The investigative scripts are organized into to a path of risk-related subjects or topics, and within each path of subjects/topics the investigative scripts are organized into a specialized inquiry or flow chart.

Systems and methods for generation and use of document analysis architectures are disclosed. A model builder component may be utilized to receiving user input data for labeling a set of documents as in class or out of class. That user input data may be utilized to train one or more classification models, which may then be utilized to predict classification of other documents. Trained models may be incorporated into a model taxonomy for searching and use by other users for document analysis purposes.

An image processing apparatus includes an input unit configured to input image data, a learning unit configured to perform machine learning processing using information contained in the image data input by the input unit, an estimation unit configured to output an estimation result based on the information contained in the image data using a learning model generated by learning of the learning unit, and a determination unit configured to determine whether the image data input by the input unit contains sensitive information, wherein in a case where the determination unit determines that the image data input by the input unit contains the sensitive information, the learning unit does not perform machine learning on at least the sensitive information contained in the image data.

A number plate information specifying device includes an image acquisition unit that acquires a number plate image, a feature point extraction unit that extracts a feature point from the number plate image, a degree of similarity calculation unit that references a learning data set in which a plurality of feature points are recorded in association with a plurality of pieces of number plate information and calculates degrees of similarity for the feature points recorded in the learning data set that correspond to the feature point extracted from the number plate image, a vote value calculation unit that, on the basis of the degrees of similarity, calculates vote values for the pieces of number plate information recorded in the learning data set, and a specifying unit that specifies the piece of number plate information that has the highest vote value as the number plate information displayed in the number plate image.

Provided is a model generation system for generating a text line recognition model that recognizes a text line included in a text line image, the model generation system including a processor section, in which the text line recognition model includes a visual feature extractor and a language context relation network, the processor section determines a variable of the language context relation network by acquiring text data for training and thus training the language context relation network by using the acquired text data, determines a variable of the visual feature extractor by training the text line recognition model through the use of a labeled text line image while the variable of the language context relation network is fixed, and generates the text line recognition model while the variable of the language context relation network is set to the determined variable thereof and the variable of the visual feature extractor is set to the determined variable thereof.

A method for intelligent screen capture in an electronic device includes: receiving, by the electronic device, a user input for capturing a screenshot of contents displayed on a screen of the electronic device; dividing, by the electronic device, the screen of the electronic device into a plurality of blocks, each of the plurality of blocks including inter-related contents displayed on the screen of the electronic device; identifying, by the electronic device, at least one block from the plurality of blocks based on a plurality of parameters; and displaying, by the electronic device, a guide user interface for capturing the screenshot of the at least one block.