Results of character recognition processing for a scanned image of a document and a setting item set to a property attached to the scanned image of a document are obtained. Displaying on a screen having a preview area where the scanned image of a document is displayed and an editing area where information input in the setting item is edited, that is, displaying the scanned image of a document in the preview area and displaying the setting item and the information in the editing area are controlled. A selection for the setting item displayed in the editing area is detected. A verification rule set to the detected setting item is obtained. A character recognition area satisfying the verification rule is extracted from the results of the character recognition processing. A character recognition area displayed on the preview area and extracted is highlighted.

Disclosed is a text recognition method and apparatus. A text recognition post-processing method for reflecting user post-correction performed by a processor in an apparatus, the text recognition post-processing method includes training a deep learning post-processing model based on post-correction data comprising a partial image including post-correction target text and post-correction text when there is user post-correction for a text recognition result of an input image; and post-processing a text recognition result of another input image by applying the trained deep learning post-processing model.

According to one embodiment, a reading support system includes a processing device. The processing device includes an extractor and a type determiner. The extractor extracts a plurality of regions from a candidate region. The candidate region is a candidate of a region in which a meter is imaged. The regions respectively include a plurality of characters of the meter. The type determiner determines a type of the meter based on positions of the regions.

A document processing method includes receiving one or more sets of documents, and assigning each document to one or more basic clusters based on the metadata of the document. It further includes for each cluster, training a respective basic cluster model detecting one or more visual element types, and responsive to a first threshold criterion measure related to the one or more basic clusters being satisfied, generating one or more superclusters based on an attribute shared by documents comprised by the plurality of basic clusters. The method also includes training a respective supercluster model detecting the one or more element types and generating a generalized cluster from the one or more superclusters. It includes training a generalized model for the generalized cluster, receiving an input document, assigning the input document to corresponding clusters, and detecting visual elements by processing the input document by each of the corresponding models.

The present disclosure is directed to systems and methods for identifying regions of interest (ROIs) in images. A computing system may identify an image including an annotation defining an ROI. The image may have a plurality of pixels in a first color space. The computing system may convert the plurality of pixels from the first color space to a second color space to differentiate the annotation from the ROI. The computing system may select a first subset of pixels corresponding to the annotation based at least on a color value of the first subset of pixels in the second color space. The computing system may identify a second subset of pixels included in the ROI from the image using the first subset of pixels. The computing system may store an association between the second subset of pixels and the ROI defined by the annotation in the image.

A system for tire identification is provided which continuously compares tires mounted on vehicles with tires known to have safety issues. Where a match is discerned for a vehicle mounted tire and a tire having safety issues, warnings are issued to one or all of the vehicle owner, the vehicle dealer, or the vehicle service center.

A region of a drug to be identified is detected from a captured image generated by imaging the drug to be identified that is imparted with engraved mark and/or print. The region of the drug to be identified in the captured image is processed to acquire an engraved mark and print extraction image that is an extracted image of the engraved mark and/or print of the drug to be identified. The engraved mark and print extraction image is input, and a drug type of the drug to be identified is inferred to acquire a candidate of the drug type of the drug to be identified.

A wine bottle tracking system (100, 500) is described herein, comprising: a plurality of wine bottle storage locations (216) within a wine storage area (214); at least one or more wine bottles (218) stored in any of the wine bottle locations; at least one optical detector (202, 502, 504) adapted to generate image information that includes image data (212) of a first wine bottle as it is being stored into, or removed from, the wine bottle storage location, and wherein the at least one optical detector is further adapted to output the image information as a camera output signal (220); at least one processor (206) communicatively coupled to the at least one optical detector; and a memory (208) operatively connected with the at least one processor, wherein the memory stores computer-executable instructions (102, 104, 118) that, when executed by the at least one processor, causes the at least one processor to execute a method (300, 400, 600) that comprises: receiving, via the at least one optical detector, the camera output signal within a wine tracker application executing on the at least one processor; analyzing the camera output signal; and extracting wine information pertaining to the wine stored in the first wine bottle on the basis of the analysis of the camera output signal by the at least one processor and wine tracking application. The systems and methods described herein can further recognize wine bottles and obtain information related to the wine in the wine bottles, and can further predict consumer behavior and/or consumption of wine.

The techniques described herein relate to a systems and methods for a digital asset generation platform. The digital asset generation platform may ingest an ingest input. The digital asset generation platform may utilize a document identification engine corresponding to a first stage of a multi-stage convolutional neural network for identifying document types of documents. The digital asset generation platform may utilize an object detector engine corresponding to a second stage of the multi-stage convolutional neural network for detecting a dynamic mapping in the digital file. The digital asset generation platform may utilize a post-processing engine for classifying the dynamic mapping in the at least one digital file. The digital asset generation platform may dynamically generate a digital asset representative of the document based on the key value data pairs extracted from the dynamic mapping.

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.