There is provided a method and a system for recommending a given text candidate as a value for a field. A document image is received and a set of text boxes are detected using optical character recognition, each of the set of text boxes comprising a respective character sequence. For each text box, based on at least the respective character sequence, at least one respective text candidate is generated to thereby obtain a set of text candidates. At least one feature extractor is used to generate a respective candidate feature vector based on each respective text candidate. An indication of the field is received, and a respective candidate score indicative of a relevance of the respective text candidate is determined. In response to a given candidate score being above a threshold, the given text candidate associated with the given candidate score is output as a recommendation for the field.

A determination system includes an imaging data acquisition device that captures an image of a printed surface of a printed product to acquire imaging data of the captured image, and a determination device that determines a printing method used to produce the printed surface of the printed product for capturing an image by the imaging data acquisition device. The determination device selects a determination area included in the captured image, extracts a determination end image from a portion around an end portion of a black determination image included in the determination area, acquires difference data that is a difference in gradation between two colors among gradation data of RBG colors of the extracted determination end image, acquires a determination value for determining a printing method used to produce the printed surface of the printed product, and determines a printing method used to produce the printed surface of the printed product.

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.

The present disclosure provides a training method and apparatus for a document processing model, a device, a storage medium and a program, which relate to the field of artificial intelligence, and in particular, to technologies such as deep learning, natural language processing and text recognition. The specific implementation is: acquiring a first sample document; determining element features of a plurality of document elements in the first sample document and positions corresponding to M position types of each document element according to the first sample document; where the document element corresponds to a character or a document area in the first sample document; and performing training on a basic model according to the element features of the plurality of document elements and the positions corresponding to the M position types of each document element to obtain the document processing model.

The present disclosure is a part identification and location system that has a server and a handheld device, and the handheld device is communicatively coupled to the server via a network. Further, the system has a handheld device processor that displays a plurality of graphical user interfaces to the handheld device and receives data from a user of the handheld device identifying a system, subsystem, or part that the user desires to purchase. Additionally, a server processor searches for the desired system, subsystem, or part on a plurality of part databases, locating one or more parts that match the system, subsystem, or part the user desires. The handheld device processor displays a list of the system, subsystems or parts located, receives data indicating which system, subsystem, or part the user desires to purchase, and receives payment data for purchasing the system, subsystem, or part.

Embodiments for automatically converting printed documents into electronic format using artificial intelligence techniques disclosed herein include: (i) receiving a plurality of images of documents; (ii) for each received image, using an image classification algorithm to classify the image as one of (a) an image of a first type of document, or (b) an image of a second type of document; (iii) for each image classified as an image of the first type of document, using an object localization algorithm to identity an area of interest in the image; (iv) for an identified area of interest, using an optical character recognition algorithm to extract text from the identified area of interest; and (v) populating a record associated with the document with the extracted text.

A system receives an image including a live facial image of the user and an identity document including a photograph of the user. Moreover, the system calculates a facial match score by comparing facial features in the live facial image to facial features in the photograph. The system recognizes data objects and characters in the identity document using optical character recognition (OCR) and computer vision, and then identifies, based on the recognized data objects and characters, a type of the identity document. Further, the system calculates a document validity score by comparing the recognized characters and data objects to character strings and data objects known to be present in the identified type of the identity document. Additionally, the system determines and outputs the user's identity verification status based on comparing the facial match score to a facial match threshold and comparing the document validity score to a document validity threshold.

A method for data collection and analysis may include generating one or more screenshots; converting the screenshot, via OCR, into one or more strings of computer-readable text; detecting one or more variables from the computer-readable text; retrieving, additional variables via one or more modules; and displaying, via a video display device, a dashboard. The dashboard may include one or more configurable views, and each configurable view may include a visual representation of one or more variables.

Disclosed are a method and device for determining an icon position. The method includes: detecting a target object in a target image and determining the reference position of the target object in the target image, and detecting a salient position in the target image, thereby obtaining the reference position of a key target or object in the target image, and a salient position possibly requiring more attention in the target image; and selecting, according to the distance between the reference position or salient position and preset candidate positions, an icon position from the candidate positions.

A focus detection method includes: acquiring an image of a test object through a to-be-tested image acquisition device, the test object including a character, and a clarity of the character corresponding to a minimum clarity with which a content of the character is still able to be recognized using a character recognition technology; performing character recognition on the image to obtain a recognition result; and determining a focus detection result for the to-be-tested image acquisition device based on the recognition result.