An information processing apparatus includes a processor, the processor extracting, from at least a part of a document, a feature of the part, extracting related elements which are elements related to the feature from a question and an answer about a first target object stored in a storage unit using the extracted feature, and combining the extracted related elements and the feature to generate a question about a second target object specified in the document.

System and method to recognise characters from an image are disclosed. The method includes receiving the at least one image, pre-processing the at least one image, extracting a plurality of characters from the corresponding at least one image, extracting at least one structure from the corresponding at least one image upon applying an edge detection technique to extract a structure, identifying a template based on extracted structure, subjecting the plurality of characters into a plurality of ensemble AI models to extract one of a plurality of texts, a plurality of non-textual data and a combination thereof, comparing a plurality of extracted plurality of texts, a plurality of non-textual data, or a combination thereof from the corresponding plurality of ensemble AI models with each other, generating a confidence score and validating one of the plurality of accurate texts, the plurality of accurate non-textual data, or a combination thereof.

A method for training a font generation model is described below. A source domain sample character and a target domain association character are input into a font generation network to obtain a target domain generation character. The target domain generation character and at least one of a target domain sample character or the target domain association character are input into a loss analysis network to obtain a first loss, and a parameter of the font generation model is adjusted according to the first loss. The source domain sample character and a random vector are input into the font generation network to obtain a random domain generation character. The random domain generation character and a random domain sample character are input into the loss analysis network to obtain a second loss, and the parameter is readjusted according to the second loss.

Systems, apparatuses and methods may provide for technology that identifies a plurality of segments based on semantic features and instance features associated with a scene, fuses the plurality of segments into a plurality of instances, and selects classification labels for the plurality of instances. In one example, the plurality of segments is fused into the plurality of instances via a learnable self-attention based network.

An example of apparatus includes a memory to store a first image of a document and a second image of the document. The first image and the second image are Memory captured under different conditions. The apparatus includes a processor coupled to the memory. The processor is to perform optical character recognition on the first image to generate a first output dataset and to perform optical character recognition on the second image to generate a second output dataset. The processor is further to determine whether consensus for a character is achieved based on a comparison of the first output dataset with the second output dataset, and generate a final output dataset based on the consensus for the character.

Systems and methods are provided for extracting information from a document. An exemplary method includes: receiving a plurality of sample documents; retrieving image information for the sample documents; converting each of the image information associated with the plurality of sample documents into a knowledge graph; determining a plurality of rules applicable for processing the sample documents; retrieving the rules applicable for processing the sample documents; parsing the rules to create a plurality of abstract syntax trees; generating a plurality of rule results for the rules applicable for processing the sample documents; applying the rule results to a probabilistic model to determine accuracy of the rule results; generating a plurality of scores for each of the rules indicating accuracy of the rules; determining whether the scores require updating the probabilistic model; and updating the probabilistic model.

The present disclosure is directed to combining the strengths of different methods of analyzing collected sensor data to updating a driving pattern of an automated vehicle (AV). This may include combining data from sets of data that track movement of objects over time with instantaneously received sensor data based on a series of steps that include accessing data that tracks the motion of objects in the field of view of a sensing apparatus, receiving current sensor data that includes a component of current or instantaneous object motion, and identifying whether controls of AV should be maintained or changed. When controls of the AV are maintained, an AV may be controlled to stay in driving in a same lane of a roadway at a same velocity. When controls of an AV are changed, changes may include applying, increasing a velocity, or altering the course of the AV.

A lane detection method includes obtaining visual detection data via a vision sensor disposed at a movable platform, performing lane line analysis and processing based on the visual detection data to obtain lane line parameters, obtaining radar detection data via a radar sensor disposed at the movable platform, performing boundary line analysis and processing based on the radar detection data to obtain boundary line parameters, and performing data fusion according to the lane line parameters and the boundary line parameters to obtain lane detection parameters.

A method and system for training a neural network are provided. The method includes receiving an input image, selecting at least one data augmentation method from a pool of data augmentation methods, generating an augmented image by applying the selected at least one data augmentation method to the input image, and generating a mixed image from the input image and the augmented image.

A system to classify image of a document using neural network architecture is provided. The system includes a storage device storing the image derived from the document having text information. The system includes a document importer operable to perform optical character recognition to convert image data in the image to machine readable data. The system includes a neural network that perform semantic enrichment and positional context for the terms of interest present in the image. The neural network is configured to take as input the machine-readable data and the image and combine both the machine-readable data and the image to classify the image of the document based on the positional context of the terms of interest.