Systems and methods for performing OCR of a series of images depicting text symbols. An example method comprises performing OCR a series of images to produce symbol sequences and corresponding symbol sequence quadrangles; identifying median string; calculating transformation of the symbol sequence quadrangles into a common coordinate system; determining distances between the transformed symbol sequence quadrangles; identifying a median symbol sequence quadrangle; displaying, using the median symbol sequence quadrangle, a resulting OCR text representing at least a portion of the original document.

A projector and a projection method are provided. The projector includes a control device, a projection optical engine, a distance sensing device, and an image capturing device. The projection optical engine projects a first projection image to a projection surface according to first image data. The distance sensing device senses multiple distance parameters of a projection area. The image capturing device captures the first projection image to obtain a first captured image. The control device performs a keystone correction operation and a leveling correction operation on the first image data. The projection optical engine projects a second projection image to the projection surface according to the corrected first image data. The control device obtains a second captured image including the second projection image through the image capturing device, and analyzes the second captured image to project current projection image size information in the second projection image.

A method for deconvolution of digital images includes obtaining a degraded image from a digital sensor, a processor accepting output from the digital sensor and recognizing a distorted element within the image. The distorted element is compared with a true shape of the element to produce a degrading function. The degrading function is deconvolved from at least a portion of the image to improve image quality of the image. A method of indirectly decoding a barcode includes obtaining an image of a barcode using an optical sensor in a mobile computing device, the image comprising barcode marks and a textual character. The textual character is optically recognized and an image degrading characteristic is identified from the textual character. Compensating for the image degrading characteristic renders previously undecodable barcode marks decodable. A system for deconvolution of digital images is also included.

A method for deconvolution of digital images includes obtaining a degraded image from a digital sensor, a processor accepting output from the digital sensor and recognizing a distorted element within the image. The distorted element is compared with a true shape of the element to produce a degrading function. The degrading function is deconvolved from at least a portion of the image to improve image quality of the image. A method of indirectly decoding a barcode includes obtaining an image of a barcode using an optical sensor in a mobile computing device, the image comprising barcode marks and a textual character. The textual character is optically recognized and an image degrading characteristic is identified from the textual character. Compensating for the image degrading characteristic renders previously undecodable barcode marks decodable. A system for deconvolution of digital images is also included.

Systems and methods for performing optical character recognition (OCR) are disclosed. An example method may include receiving a current image that overlaps with a previous image of a series of images of an original document; performing OCR of the current image to produce an OCR text; identifying a plurality of textual artifacts in the images that are each represented by a sequence of symbols having a frequency of occurrence within the OCR text falling below a threshold frequency; identifying corresponding base points that are each associated with a textural artifact; identifying parameters of a coordinate transformation converting coordinates of the previous image into coordinates of the current image; associating part of the OCR text with a cluster of symbol sequences, the symbol sequences being produced by processing previously received images; identifying a median string representing the cluster; and producing a resulting OCR text representing a portion of the original document.

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.

To speed up image processing, an obtaining means of an image processing system obtains a captured image of a document that includes a fixed part and an un-fixed part, where the document is captured by an image reader or an image capture device. A first shaping means shapes the captured image based on a feature of the document in a sample image and a feature of the document in the captured image so as to obtain a first shaped image. A detecting means detects a feature part of the fixed part from the first shaped image. A second shaping means shapes the first shaped image such that a position of the feature part detected by the detecting means is aligned with a predetermined position so as to obtain a second shaped image.

The present document is directed to methods and subsystems that identify and characterize document-containing subimages in a document-containing image. In one implementation, each type of document is modeled as a set of features that are extracted from a set of images known to contain the document. To locate and characterize a document subimage in an image, the currently described methods and subsystems extract features from the image and then match model features of each model in a set of models to the extracted features to select the model that best corresponds to the extracted features. Additional information contained in the selected model is then used to identify the location of the subimage corresponding to the document and to process the document subimage to correct for a variety of distortions and deficiencies in order to facilitate subsequent data extraction from the corrected document subimage.

A feature point is extracted from an input image including an image region for which depth values of pixels change consecutively. A transformation that normalizes depth values of pixels of a vicinity of the feature point with respect to a region of at least a portion of the input image is set as a normalization transformation, and an image for a feature amount calculation is generated by performing the normalization transformation on a pixel position of the feature point. A feature amount is calculated from the image for the feature amount calculation.

A projector in an installation information acquisition method is installed in a real space, has a changeable projection direction, and projects a projection image based on a virtual image. The virtual image is an image in a case where an image arranged at a display position in a virtual space is viewed from a virtual installation position. The method includes first acquisition processing for acquiring positional information of three or more first adjustment points in the virtual space, projection processing for projecting, by the projector, an index image onto the real space, second acquisition processing for acquiring angle information of the projection direction with respect to a reference direction in a state where the index image matches three or more second adjustment points respectively corresponding to the three or more first adjustment points, and third acquisition processing for acquiring installation information based on the positional information and the angle information.