Method, computer readable medium, and apparatus of recognizing character zone in a digital document. In an embodiment, the method includes classifying a segment of the digital document as including text, calculating at least one parameter value associated with the classified segment of the digital document, determining, based on the calculated at least one parameter value, a zonal parameter value, classifying the segment of the digital document as a handwritten text zone or as a printed text zone based on the determined zonal parameter value and a threshold value, the threshold value being based on a selection of an intersection of a handwritten text distribution profile and a printed text distribution profile, each of the handwritten text distribution profile and the printed text distribution profile being associated with a zonal parameter corresponding to the determined zonal parameter value, and generating, based on the classifying, a modified version of the digital document.

Embodiments of the invention are directed to systems, methods, and computer program products for a unique platform for analyzing, classifying, extracting, and processing information from graphical representations. Embodiments of the inventions are configured to provide an end to end automated solution for extracting data from graphical representations and creating a centralized database for providing graphical attributes, image skeletons, and other metadata information integrated with a graphical representation classification training layer. The invention is designed to receive a graphical representation for analysis, intelligently identify and extract objects and data in the graphical representation, and store the data attributes of the graphical representation in an accessible format in an automated fashion.

This application relates to the field of artificial intelligence technologies, and in particular, to a training method of an image-text matching model, a bi-directional search method, and a relevant apparatus. The training method includes extracting a global feature and a local feature of an image sample; extracting a global feature and a local feature of a text sample; training a matching model according to the extracted global feature and local feature of the image sample and the extracted global feature and local feature of the text sample, to determine model parameters of the matching model; and determining, by the matching model, according to a global feature and a local feature of an inputted image and a global feature and a local feature of an inputted text, a matching degree between the image and the text.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

A segmentation neural network is extended to provide classification at the segment level. An input image of a document is received and processed, utilizing a segmentation neural network, to detect pixels having a signature feature type. A signature heatmap of the input image can be generated based on the pixels in the input image having the signature feature type. The segmentation neural network is extended from here to further process the signature heatmap by morphing it to include noise surrounding an object of interest. This creates a signature region that can have no defined shape or size. The morphed heatmap acts as a mask so that each signature region or object in the input image can be detected as a segment. Based on this segment-level detection, the input image is classified. The classification result can be provided as feedback to a machine learning framework to refine training.

Position detection methods and systems are disclosed herein. The position detection method of detecting a position in an operation surface pointed by a pointing element includes obtaining a first taken image with the first infrared camera, obtaining a second taken image with the second infrared camera, removing a noise component from the first and second images converting the first and second taken into converted images without the noise component, forming a difference image between the first converted taken image and the second converted taken image, extracting a candidate area in which a disparity amount between the first converted taken image and the second converted taken image is within a predetermined range, detecting a tip position of the pointing element from the candidate area, and determining a pointing position of the pointing element and whether or not the pointing element had contact with the operation surface based on the detecting.

Systems and methods for detection and localization of image and document forgery. The method can include the step of receiving a dataset having a plurality of authentic images and a plurality of manipulated images. The method can also include the step of benchmarking a plurality of image forgery algorithms using the dataset. The method can further include the step of generating a plurality of receiver operating characteristic (ROC) curves for each of the plurality of image forgery algorithms. The method also includes the step of calculating a plurality of area under curve metrics for each of the plurality of ROC curves. The method further includes the step of training a neural network for image forgery based on the plurality of area under curve metrics.

A card number recognition method and apparatus, a storage medium, and an electronic device are disclosed. The method includes: obtaining distribution format information of character bits of a card number sequence, where the distribution format information includes character bit spacing information of the card number sequence; recognizing a character sequence in a target image through a neural network model trained in advance, and obtaining character bit spacing information of the recognized character sequence; determining whether the character bit spacing information of the recognized character sequence is consistent with the character bit spacing information in the obtained distribution format information; and if the character bit spacing information of the character sequence is consistent with the character bit spacing information in the obtained distribution format information, determining that the recognized character sequence is target card numbers.

The present teaching relates to system, method, medium for in-situ perception in an autonomous driving vehicle. A plurality of types of sensor data are received, which are acquired by a plurality of types of sensors deployed on the vehicle to provide information about surrounding of the vehicle. Based on at least one model, one or more surrounding items are tracked from a first of the plurality of types of sensor data acquired by a first type sensors. At least some of the tracked items are automatically labeled via cross validation and are used to locally adapt, on-the-fly, the at least one model. Model update information is received which from a model update center, which is derived based on the labeled at least some items. The at least one model is updated using the model update information.

According to an embodiment, an information processing apparatus comprises a first interface, a second interface, a third interface, and a processor. The first interface acquires a character string image that includes a character string. The second interface transmits and receives data to and from an internal device through a first network. The third interface transmits and receives data to and from an external device through a second network. The processor transmits, if the character string image includes personal information, the character string image to the internal device through the second interface and receive an input of the character string from the internal device and transmits, if the character string image does not include the personal information, the character string image to the external device through the third interface and receive an input of the character string from the external device.