This disclosure relates generally to method and system for detecting and extracting price region from digital flyers and promotions. In retail business, extracting price information from digital flyers is crucial for complex nature of flyers having large variety of formats, color scheme, font styles, variable text information and thereof. The method of the present disclosure detects a text region comprising a price information from a set of digital flyers and promotions received as input images. Further, each text region is converted into a two-color text comprising of a set of white pixels and a set of black pixels. Further, underlying price from the price region of the two-color text is detected and price is extracted from the price region of each input image. Additionally, the price region detection function detects price region accurately and extracts price values having an irregular font size.

A method for training an image editing model includes steps described below. Covering processing is performed on a region of interest determined in an original image so that a background image sample is formed, and content corresponding to the region of interest is determined as a sample of content of interest; the background image sample and the sample of the content of interest are input into an image editing model; fusion processing is performed on a background image feature and a feature of the region of interest by using the image editing model so that a fusion feature is formed; an image reconstruction operation is performed according to the fusion feature by using the image editing model so that a reconstructed image is output; and optimization training is performed on the image editing model according to a loss relationship between the reconstructed image and the original image.

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.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for machine learning. One of the methods includes receiving an image; providing the image to a neural network model, wherein the neural network model is trained to output predictions of one or more locations within the image and corresponding classifications; extracting text content within one or more of the one or more locations; analyzing the extracted text content using the corresponding classifications to evaluate one or more of external consistency with other data records or internal consistency with content from one or more of the particular locations; and generating one or more outputs based on the analyzing.

A system for zonal OCR in an AR context is provided having a monitored device having text in at least one zone for indicating device states; a computer receives a picture of said monitored device and said text and processes said picture to determine a plurality of feature points; a database is in data communication with said computer and stores a plurality of reference images with reference feature points and reference zones; the computer matches at least some of the plurality of feature points with at least some of the plurality of reference feature points to compute a homography matrix; the computer transforms the image into a transformed image using the homography matrix; the computer executes optical character recognition on a zone of the transformed image based on the reference zones to generate extracted text.

An online document system can allow users to track various amendments made over time and corresponding to an original document. The online document system accesses the original document comprising a plurality of content sections and a set of amendment documents each comprising one or more amendments to the original document. The online document system applies a machine-learned model to the original document and the set of amendment documents to identify, for each amendment, a content section of the plurality that corresponds to the amendment and a type of amendment corresponding to the amendment. The online document system generates an amended original document comprising the plurality of content sections modified to include each amendment. The online document system displays the amended original document by displaying each of the plurality of content sections and, in conjunction with each content section, any amendments corresponding to the content section are highlighted.

Embodiments of the innovation relate to a data extraction device, comprising a controller having a processor and memory. The controller is configured receive an unstructured data file comprising a set of documents; apply the unstructured data file to a document identification model to identify a data element identifier and an associated data element of each document of the set of documents; apply an optical character recognition engine to the identified data element identifier and associated identified data element to generate a structured data element identifier and an associated structured data element, the structured data element identifier and the associated structured data element configured as machine-identifiable characters; embed the structured data element identifier and associated structured data element as metadata with the unstructured data file; and store the unstructured data file and metadata in a database.

Method and system of automatically extracting information of a predefined type from a document is provided. The method includes identifying a location and classification of a segment of interest of a document that includes information associated with a predefined type. The method further includes identifying a location and classification of characters from the segment of interest based on characteristics associated with the predefined type. The method further includes extracting the identified characters from the segment of interested associated with the predefined type.

A method and a device for generating a collection of incorrectly-answered questions are provided. The method includes: acquiring an image of a marked test paper (S101); recognizing regions of respective questions in the marked test paper according to a pre-trained first region recognition model (S102); recognizing a question whose marking result is incorrect in the marked test paper as an incorrectly-answered question according to a pre-trained incorrectly-answered question recognition model (S103); and storing the region of the incorrectly-answered question in an incorrectly-answered question database to generate the collection of incorrectly-answered questions (S104). The above solution may solve the problem of low efficiency in generating the collection of incorrectly-answered questions in the prior art.

A captured image contains a region of interest (ROI) including a plurality of characters to be recognized as text, and non-ROI content to be excluded from the OCR. The captured image is preprocessed to detect and locate the ROI in the captured image, and to determine a boundary of the ROI, including transforming the captured image to a first feature descriptor representation (FDR), and performing a comparison between the first FDR and at least one ROI template that includes at least a second FDR of a representative ROI image. The preprocessing produces an output to be provided to an OCR engine to perform autonomous OCR processing of the ROI while ignoring the non-ROI content based on the determined boundary of the ROI.