Aspects relate to apparatuses and methods for selectively inserting text into a video resume. An exemplary apparatus includes a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to receive a video resume from a user, divide the video resume is into temporal sections, acquire a plurality of textual inputs from a user, wherein the plurality of textual inputs pertains to the same user of received video resume, classify the plurality of textual inputs to corresponding temporal sections of the received video resume and display, as a function of the classification, the received video resume with a corresponding plurality of textual inputs.

Aspects relate to apparatuses and methods for selectively inserting text into a video resume. An exemplary apparatus includes a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to receive a video resume from a user, divide the video resume is into temporal sections, acquire a plurality of textual inputs from a user, wherein the plurality of textual inputs pertains to the same user of received video resume, classify the plurality of textual inputs to corresponding temporal sections of the received video resume and display, as a function of the classification, the received video resume with a corresponding plurality of textual inputs.

An identity document can be authenticated using format data of a barcode on the document, such as a barcode on a driver's license. Scan data is obtained by decoding a plurality of barcodes. Format features of the plurality of barcodes are extracted. Scan data is classified into two or more clusters. Each cluster is characterized by a set of format features extracted from the scan data. A barcode on an ID to be verified is scanned. Format features from the barcode of the ID to be verified is compared to at least one of the two or more clusters to authenticate the ID.

Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.

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.

An image content determination device includes at least one processor, in which the processor is configured to execute first recognition processing of recognizing a character and a face of a first person from a first image including the character and the face of the first person, execute first acquisition processing of acquiring first person-related information related to the first person included in the first image based on the recognized character and face of the first person, execute second recognition processing of recognizing a face of a second person from a second image including the face of the second person, and execute second acquisition processing of acquiring second person-related information related to the second person included in the second image, in which the second person-related information is acquired using the first person-related information corresponding to the first image including the face of the first person similar to the face of the second person.

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.

An apparatus and methods for generating automated communication simulation using artificial intelligence is disclosed. The apparatus comprises at least a processor, a memory communicatively connected to the processor, wherein the memory includes instructions configuring the at least a processor to receive a prior communication datum from a first user, parse the prior communication datum to extract at least a contextual datum relating to a second user, generate a correspondence simulation from the first user to the second suer as a function of the at least a contextual datum, receive a prior handwriting image datum from the first user, classify at least a portion of the prior handwriting image datum to at least a glyph, identify at least a semantic match to the at least a glyph in the correspondence simulation, generate and transmit an automated communication simulation using the at least a semantic match and the correspondence simulation.

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.