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.

An image processing apparatus includes a layout analyzing part that executes layout analysis for image data, an extraction part that extracts a diagrammatic representation from the image data by using a result of the layout analysis, a character recognizing part that executes character recognition for a partial area having a high probability of presence of a character string in a relationship with the extracted diagrammatic representation, and an erecting direction deciding part that decides an erecting direction of the image data by using a result of the character recognition.

A text classifying apparatus (100), an optical character recognition unit (1), a text classifying method (S220) and a program are provided for performing the classification of text. A segmentation unit (110) segments an image into a plurality of lines of text (401-412; 451-457; 501-504; 701-705) (S221). A selection unit (120) selects a line of text from the plurality of lines of text (S222-S223). An identification unit (130) identifies a sequence of classes corresponding to the selected line of text (S224). A recording unit (140) records, for the selected line of text, a global class corresponding to a class of the sequence of classes (S225-S226). A classification unit (150) classifies the image according to the global class, based on a confidence level of the global class (S227-S228).

An intelligent parking management system for residential communities is disclosed that includes a license plate reader; and a server communicatively coupled to the license plate reader over a network. The server includes a memory storing a parking policy and registered license places for one or more residential communities registered with the at least one server; and at least one processor. The processor is operably configured to receive a license plate number, over the network, from the license plate reader; compare the license plate number to a plurality of registered license plate numbers stored in the memory; and communicate, over the network, a parking violation message as a result of determining that the license plate number does not match any one of the plurality of registered license plate numbers to a user such as a resident, a towing company, or administrator.

A method of image-to-document conversion based on optical character recognition (OCR) includes obtaining an image to be converted into a target document, and performing layout segmentation on the image according to image content of the image, to obtain n image layouts, each of the n image layouts corresponding to a content type, and n being a positive integer. The method also includes, for each of the n image layouts, processing image content in the respective image layout according to the content type corresponding to the respective image layout, to obtain converted content corresponding to the respective image layout. The method further includes adding the converted content corresponding to the n image layouts to an electronic document, to obtain the target document.

An electronic device and method are disclosed. The electronic device includes a touch-sensitive display, a memory and a processor. The processor implements the method, including: detect a written input including a plurality of strokes through the display, group the plurality of strokes into a first group and a second group based on respective coordinates of each of the plurality of strokes, group first strokes included in the first group into a plurality of blocks, based on a distance between respective coordinates of each of the first strokes, determine a slope for each of the plurality of blocks, rotate an area corresponding to the first group based on the determined slope, execute handwriting recognition on the first strokes based on the rotated area, and displaying a result of the handwriting recognition on the display.

A system for extracting text from images comprises a processor configured to receive a digital copy of an image and identify a portion of the image, wherein the portion comprises text to be extracted. The processor further determines orientation of the portion of the image, and extracts text from the portion of the image considering the orientation of the portion of the image.

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.

Disclosed herein are system, method, and computer program product embodiments for optical character recognition (OCR) pre-processing using machine learning. In an embodiment, a neural network may be trained to identify a standardized document rotation and scale expected by an OCR service performing character recognition. The neural network may then analyze a received document image to identify a corresponding rotation and scale of the document image relative to the expected standardized values. In response to this identification, the document image may be modified in the inverse to standardize the rotation and scale of the document image to match the format expected by the OCR service. In some embodiments, a neural network may perform the standardization as well as the character recognition using a shared computation graph.

A control device sets first points on characters, generates a first frame composed of a first point and first circles, attaches first marks to points at which the first circles intersect characters, detects a range having a largest central angle and no first marks, sets second points on the first circles in the detected region, generates a second frame composed of a second point and second circles, attaches second marks to points at which the second circles intersect characters, sets a direction passing through the center portions of ranges having no second marks and the second point, sets second points arranged in the direction as the same class, calculates an approximate line connecting second points for each class, obtains straight lines indicating a row direction of characters immediately above and below an approximate line, and determines an inclination angle of an image from inclinations of the straight lines.