The disclosure provides an augmented reality system (200) including an input unit (204), a text recognition unit (206), a natural language processing unit (208), a positioning unit (210), and an output unit (212). The input unit (204) captures an image. The text recognition unit (206) identifies an information on a surface depicted in the image and generates an input data based on the information. The natural language processing unit (208 ) determines a context of the input data and generates at least one assistive information based on the context. The positioning unit (210) determines one or more spatial attributes based on the image and generates a positioning information based on the spatial attributes. The output unit (212) displays the assistive information based on the positioning information.

Embodiments of an image reader and/or methods of operating an image reader can capture an image, identify a bar code or IBI form within the captured image, and, store or display the captured image responsive to the an orientation of the bar code.

The disclosure provides an augmented reality system (200) including an input unit (204), a text recognition unit (206), a natural language processing unit (208), a positioning unit (210), and an output unit (212). The input unit (204) captures an image. The text recognition unit (206) identifies an information on a surface depicted in the image and generates an input data based on the information. The natural language processing unit (208) determines a context of the input data and generates at least one assistive information based on the context. The positioning unit (210) determines one or more spatial attributes based on the image and generates a positioning information based on the spatial attributes. The output unit (212) displays the assistive information based on the positioning information.

An example method is of image processing provided in according with one implementation of the present disclosure. The method includes receiving an image, placing a window across the image, and computing a set of all occurring grayscale values within the window. The method further includes computing a threshold value based on the set of all occurring grayscale values within the window and determining an output pixel value of at least one pixel from the window based on the threshold value.

A detected quadrilateral area is displayed and no group of candidate lines is displayed in a normal state. While a user is selecting a side that the user desires to change, a group of candidate lines corresponding to the selected side is displayed. Then, whether to replace a position of the selected side with a position of a candidate line is determined based on a movement destination position of the selected side.

Determination of an underlying grid structure that facilitates layout of East Asian text is disclosed. The underlying grid structure includes both a size of character frames and a size of a text block frame. The East Asian text may be obtained from a scan of printed material that has the text formatted according to layout conventions established by the publisher. The text may be reformatted to appear on a display of an electronic device in a manner similar to the formatting in the original scanned document. Reformatting may include reflowing the text in order to fit a greater or lesser number of characters on a line. The reflowing may maintain character spacing from the original document and follow formatting rules against locating certain characters at the start or end of a line.

A method and apparatus for recognizing an imaged information-bearing medium, a computer-readable storage device and a computer device are provided. The method comprising: acquiring a first image of the imaged information-bearing medium; performing text recognition on the first image to acquire a text content of the imaged information-bearing medium; classifying the imaged information-bearing medium to acquire a type of the imaged information-bearing medium; and archiving the text content according to the type.

An image processing apparatus executes a first morphology for a first binary image, to generate a second binary image, specifies a vertical line missing region based on the second binary image, executes a second morphology under a condition different from a condition in the first morphology for the second binary image, to generate a third binary image, acquires pixel information about a region corresponding to the vertical line missing region in the third binary image, and corrects a region corresponding to the vertical line missing region in the first binary image using the acquired pixel information, to generate a fourth binary image.

Optical character recognition (OCR) based systems and methods for extracting and automatically evaluating contextual and identification information and associated metadata from an image utilizing enhanced image processing techniques and image segmentation. A unique, comprehensive integration with an account provider system and other third party systems may be utilized to automate the execution of an action associated with an online account. The system may evaluate text extracted from a captured image utilizing machine learning processing to classify an image type for the captured image, and select an optical character recognition model based on the classified image type. They system may compare a data value extracted from the recognized text for a particular data type with an associated online account data value for the particular data type to evaluate whether to automatically execute an action associated with the online account linked to the image based on the data value comparison.

Method and system for reading and validating identity documents that involves acquiring an image of a first and/or a second side of an identity document using a camera of a portable device; recognizing whether MRZ characters exist or are readable in the acquired image; if said MRZ characters are readable or do exist reading them obtaining a pre-identified document, or otherwise, detecting a series of local points of interests and their positions on the image calculating descriptors or vectors; and identifying the type or model of said identity document, starting by correcting perspective distortions caused by a bad relative position of the identity document with respect to the camera for obtaining a corrected and substantially rectangular image of the first and/or second side of the document at a predetermined scale which is used to perform, automatically, said identification of the identity document type or model and to automatically read and identify text and non-text information included in said corrected and substantially rectangular image.