The present disclosure relates to a font detection method using a neural network. The font detection method using the neural network according to the present disclosure includes receiving a target text image including a text; resizing a horizontal or vertical size to a reference input size according to an aspect ratio of the input target text image; and inputting the resized target text image into a trained neural network and outputting a font of the text included in the text image, and the neural network may be trained with a unit image extracted as a unit region of the reference input size from a training image generated by synthesizing a background with the text. According to the present disclosure, fonts according to various usage examples may be effectively detected.

A recognition system for recognizing multiple inputs of gestures, handwriting symbols and virtual keys on a touch screen includes a touch IC serves to convert a plurality of touch signals of the touch screen to a touch data frame. A processor set is connected to the touch IC and serves to perform a touch data processing on the touch data frame. The touch data processing is performed by using a processing directly executed by an OS (Operating System) and a processing of AI (artificial intelligence) recognizing. An AI recognition module is connected to the processor set. The AI recognition module is used for recognizing multiple key inputs, operation gestures and handwriting symbols. The key inputs and handwriting symbols are corrected by a grammar correction and a symbol correction respectively. The touch screen serves to display a virtual keyboard.

The present disclosure relates to technology for automatically searching and recovering the recovery area of frames corresponding to a desired time for large-capacity video evidence using a time map generated through an optical character recognition (OCR) function. A digital forensic apparatus for searching and recovering a recovery target area for large-capacity video evidence using a time map according to an embodiment of the present disclosure may include a division recovery device for collecting video evidence from a storage device, dividing the collected video evidence into a plurality of spaces in consideration of the physical space of the storage device, and recovering a representative frame in each of the divided spaces; a time information recognizer for recognizing time information from the recovered representative frame using an optical character recognition (OCR) function; a time map generator for generating a time map in which the divided spaces are arranged according to a time criterion based on the recognized time information; and a selective recovery device for searching a recovery target area by matching specific time information input by a user with the generated time map and recovering the searched recovery target area.

The present disclosure relates to technology for automatically searching and recovering the recovery area of frames corresponding to a desired time for large-capacity video evidence using a time map generated through an optical character recognition (OCR) function. A digital forensic apparatus for searching and recovering a recovery target area for large-capacity video evidence using a time map according to an embodiment of the present disclosure may include a division recovery device for collecting video evidence from a storage device, dividing the collected video evidence into a plurality of spaces in consideration of the physical space of the storage device, and recovering a representative frame in each of the divided spaces; a time information recognizer for recognizing time information from the recovered representative frame using an optical character recognition (OCR) function; a time map generator for generating a time map in which the divided spaces are arranged according to a time criterion based on the recognized time information; and a selective recovery device for searching a recovery target area by matching specific time information input by a user with the generated time map and recovering the searched recovery target area.

Systems and methods for using a mobile device to submit an application for an insurance policy using images of documents captured by the mobile device are provided herein. The information is then used by an insurance company to generate a quote which is then displayed to the user on the mobile device. A user captures images of one or more documents containing information needed to complete an insurance application, after which the information on the documents is extracted and sent to the insurance company where a quote for the insurance policy can be developed. The quote can then be transmitted back to the user. Applications on the mobile device are configured to capture images of the documents needed for an insurance application, such as a driver's license, insurance information card or a vehicle identification number (VIN). The images are then processed to extract the information needed for the insurance application.

Systems and methods for using a mobile device to submit an application for an insurance policy using images of documents captured by the mobile device are provided herein. The information is then used by an insurance company to generate a quote which is then displayed to the user on the mobile device. A user captures images of one or more documents containing information needed to complete an insurance application, after which the information on the documents is extracted and sent to the insurance company where a quote for the insurance policy can be developed. The quote can then be transmitted back to the user. Applications on the mobile device are configured to capture images of the documents needed for an insurance application, such as a driver's license, insurance information card or a vehicle identification number (VIN). The images are then processed to extract the information needed for the insurance application.

Aspects and implementations provide for techniques of fast and efficient identification of objects of multiple types in electronic documents. The disclosed techniques include, for example, processing, using a machine learning model (MLM), an image of a document to generate a plurality of pixel-level maps (PLMs), characterizing associations of pixels of the image with various object types. The MLM includes a backbone neural network (NN) processing the image and generating a feature tensor for the image. The MLM further includes a plurality of classification NNs that process the feature tensor and generate PLMs. The techniques further include generating, using the PLMs, an object-level map identifying placement of one or more objects in the document. The classification NNs may be trained together (end-to-end) with the backbone NN.

Aspects and implementations provide for techniques of fast and efficient identification of objects of multiple types in electronic documents. The disclosed techniques include, for example, processing, using a machine learning model (MLM), an image of a document to generate a plurality of pixel-level maps (PLMs), characterizing associations of pixels of the image with various object types. The MLM includes a backbone neural network (NN) processing the image and generating a feature tensor for the image. The MLM further includes a plurality of classification NNs that process the feature tensor and generate PLMs. The techniques further include generating, using the PLMs, an object-level map identifying placement of one or more objects in the document. The classification NNs may be trained together (end-to-end) with the backbone NN.

Methods, systems, and computer-program products for image enhancement include receiving an image and optionally a user request, classify the image, crop image components of the image, restore cropped image components of the image, colorized restored image components, and reconstruct the image from the colorized, restored image components and other components. The other components may include text components that are restored in a separate treatment pipeline.

According to an embodiment, an electronic device receives a request to restore a second input image with a first resolution representing a specified portion of a first input image to an output image with a second resolution exceeding the first resolution. The electronic device, based on the received request, executes an image restoration model including a first encoder for extracting first feature information from the first input image, a second encoder for extracting second feature information from the second input image, and a decoder for generating the output image with the second resolution based on multi head cross attention between the first feature information and the second feature information. The electronic device provides the output image with the second resolution obtained based on the execution of the image restoration model, as a response to the request.