Certain aspects of the present disclosure provide techniques for training and using machine learning models to extract key-value sets from a document. An example method generally includes identifying regions of a document including key-value sets corresponding to inputs to a data processing application based on a first machine learning model and an electronic version of the document. One or more keys and one or more values are identified in the document based on a second machine learning model. One or more key-value sets are generated based on matching keys of the one or more keys and values of the one or more values in the region of the document. The one or more key-value sets are provided to a data processing application for processing.

Provided is an information processing apparatus that applies correction using a character recognition error pattern to a character recognition result of a document image, wherein the character recognition error pattern includes error pattern information on a character recognition result of a part where an error occurs in character recognition, correct pattern information applicable to the part where the error occurs, information on a frequency that the error occurs, and information on a state where the error occurs, and wherein the character recognition error pattern to be used in the correction is narrowed down based on the information on the frequency that the error occurs and the information on the state where the error occurs.

Implementations of the present disclosure relate to methods, devices, and computer program products of extracting a feature for multimedia data that comprises a plurality of medium types. In a method, a first feature is determined for a first medium type in the plurality of medium types by masking a portion in a first medium object with the first medium type. A second feature is determined for a second medium type other than the first medium type in the plurality of medium types. The feature is generated for the multimedia data based on the first and second features. With these implementations, multiple medium types are considered in the feature extraction, and thus the feature may fully reflect various aspects of the multimedia data in an accurate way.

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.

Provided are a hot word extraction method and apparatus, an electronic device, and a storage medium. The method includes that a target key video frame is determined, that a target region in the target key video frame is determined, that target content in the target key video frame is determined based on the target region, and that a hot word of the target key video frame is determined by processing the target content.

Systems and methods for extracting information from a sheet of material to facilitate organization of information from paper, and other physical writing surfaces are provided. An example system includes a sheet of material and a device for scanning the sheet with an optical sensor. The sheet of material includes an indication region. The indication region allows for indictors to be marked corresponding with at least one a corresponding subregion to be extracted. The sheet of material further includes at least one fiducial mark for identifying a boundary of the sheet. The device includes a processor operably coupled to the optical sensor for causing the optical sensor to scan the sheet and detect a boundary thereof using the fiducial marks and further identify a designated subregion of the sheet. Upon identification of the designated subregion, the processor is configured to extract information contained in the designated subregion for organization of information.

A system and method for ensuring the authenticity of imaged documents by embedding data into the images of such documents. The embedded data can then be extracted and decoded, and used to determine whether a particular document may be fraudulent. The documents may be checks, money orders, contracts, invoices, titles or other legal documents that may need to have their authenticity confirmed to prevent, for example, forged or otherwise false documents from being accepted as genuine.

An asset identification and tracking system includes one or more monitoring units configured to monitor at least one designated area. Each of the monitoring units includes an imaging device and one or more processors. The imaging device is configured to generate image data depicting one or more mobile assets that move through the at least one designated area. The one or more processors are operably coupled to the imaging device and configured to analyze the image data to detect and decipher one or more identifiers that are displayed on a particular mobile asset of the one or more mobile assets that move through the at least one designated area. The one or more processors are further configured to generate a detection message that includes the one or more identifiers for communication to an asset control system.

Described herein are computerized methods and systems for authentication of a physical document. An image capture device coupled to a mobile device captures a sequence of images of a physical document as at least one of the physical document or the image capture device is rotated, during which the mobile device tracks the physical document throughout the sequence of images, and adjusts operational parameters of the image capture device based upon imaging conditions associated with the physical document. The mobile device selects images from the sequence of images and classifies the physical document using the selected images. The mobile device identifies a region of interest in the physical document using the selected images and the classification. The mobile device reconstructs the region of interest, generates an authentication score for the document using the reconstructed region of interest, and determines whether the physical document is authentic based upon the authentication score.

Described herein are computerized methods and systems for authentication of a physical document. An image capture device coupled to a mobile device captures images of a physical document, during which the mobile device adjusts operational parameters of the image capture device, resulting in a sequence of images captured using different capture settings. The mobile device partitions the sequence of images into subsets of images, wherein each subset comprises images with a similar alignment of the physical document and captured using the same capture settings. The mobile device processes the subsets of images to identify a region of interest in each image. The mobile device generates a representation of the identified region of interest using the processed images, generates an authentication score for the document using the representation of the identified region of interest, and determines whether the physical document is authentic based upon the authentication score.