Systems and methods provide a deployable cloud-agnostic redaction container for performing optical character recognition and redacting information from a document using a cloud-based, guided redaction framework. An example method for document redaction includes receiving a plurality of documents and extracting pages from the plurality of documents. The method then determines, based on a load balancing criterion, a processing order for the pages extracted from the plurality of documents, and performs, based on the processing order, an optical character recognition process and a redaction process on the pages to generate redacted pages. The redacted pages are provided for transmission or storage to a cloud data management platform.

There is disclosed a method of determining a digital document suitability for OCR processing, the method executable by a user electronic device, the user electronic device configured for capturing a digital image of a document. The method comprises: acquiring by the user electronic device, the digital image of the document; determining, by a classifier executed by the user electronic device, an OCR suitability parameter associated with the digital image, the OCR suitability parameter indicative of whether the digital image is suitable for producing an output of the OCR processing of an acceptable quality, the classifier having been trained to determine the OCR suitability parameter at least partially based on a level of noise associated with the digital image; in response to the OCR suitability parameter being below a pre-determined threshold, causing the user electronic device to re-acquire the digital image.

According to the present disclosure, an image-processing apparatus acquires horizontal black hat image data by taking a difference between source image data and horizontal closing image data obtained by performing closing processing on each pixel row of the source image data, acquires vertical black hat image data by taking a difference between the source image data and vertical closing image data obtained by performing the closing processing on each pixel column of the source image data, and acquires logical conjunction image data where a logical conjunction of the horizontal black hat image data and the vertical black hat image data is taken.

A processor determines a cause of an image defect based on a test image that is obtained through an image reading process performed on an output sheet output from an image forming device. The processor generates an extraction image by extracting, from the test image, a noise point that is a type of image defect. Furthermore, the processor determines a cause of the noise point by using at least one of, in the extraction image: an edge strength of the noise point; a degree of flatness of the noise point; and a pixel value distribution of a transverse pixel sequence that is a pixel sequence traversing the noise point.

A method is described to increase the efficiency of the removal of defects from document images by reorienting the conceptual framework within which an image is filtered. Rather than arbitrarily applying a filter to an entire landscape of a document image, the disclosure describes a methodology by which a document image is separated into regions of darkness and regions of light, or viewed alternatively, regions of darkness and regions of lack of darkness. Filtering is then adaptively applied to each region to remove defects.

A portable writing apparatus includes a writing instrument carriage to hold a writing instrument and an actuator system coupled to the writing instrument carriage to move the writing instrument carriage. A controller is electrically coupled to the actuator system, and the controller includes logic that when executed by the controller causes the controller to perform operations including: receiving input data including a user's handwritten characters; and controlling the actuator system to move the writing instrument carriage in response to the input data. The writing instrument carriage traces a shape of the user's handwritten characters.

A method for defending against an adversarial sample in image classification includes: denoising, by an adversarial denoising network, an input image to acquire a reconstructed image; acquiring, by a target classification model, a predicted category probability distribution of the reconstructed image; acquiring, by the target classification model, a predicted category probability distribution of the original input image; calculating an adversarial score of the input image, and determining the input image as an adversarial sample or a benign sample according to a threshold; outputting a category prediction result of the reconstructed image if the input image is determined as the adversarial sample; and outputting a category prediction result of the original input image if the input image is determined as the benign sample. A system for defending against an adversarial sample in image classification, and a data processing terminal are further provided.

A system and method for automating and improving tabular and list-based data extraction from a variety of document types is disclosed. The system and method detect and sort which documents include tables or lists, and performs row and column segmentation. In addition, the system and method apply Conditional Random Fields models to localize each table and semantic data understanding to map and export the extracted data to the desired format and arrangement.

A system and method for automating and improving tabular and list-based data extraction from a variety of document types is disclosed. The system and method detect and sort which documents include tables or lists, and performs row and column segmentation. In addition, the system and method apply Conditional Random Fields models to localize each table and semantic data understanding to map and export the extracted data to the desired format and arrangement.

The present invention provides an image processing device whereby the probability of outputting a restored image which accurately corresponds to an original image which is included in a low-quality input image is improved. This image processing device comprises: an image group generating means for generating, from the input image, using a dictionary which stores a plurality of patch pairs wherein a degradation patch which is a patch of a degraded image wherein a prescribed image is degraded is associated with a restoration patch which is a patch of this prescribed image, a plurality of restored image candidates including a plurality of different instances of content which have a possibility of being the original content of the input image; and an image selection presentation means for clustering the generated plurality of restored image candidates, and selecting and outputting an image candidate on the basis of the result of this clustering.