The first neural network is a learned neural network learned in such a way as to output, when an object image is input, a geometric transformation parameter relevant to the object image. The object image is an image of an object identified based on object information of the first teaching data including an image and the object information including a category, a position, and a size of an object included in the image. The calculation unit calculates an orientation of the object, based on the geometric transformation parameter being output from the first neural network. The generation unit generates, by adding the orientation of the object being calculated by the calculation unit to the first teaching data, second teaching data including an image and object information including a category, a position, a size, and an orientation of an object included in the image.

The present invention provides an orientation adjustment device and an orientation adjustment method of a displayed image. The orientation adjustment method includes the following steps. Step 1 is to capture a first image frame and a second image frame sequentially by an image capturing unit. Step 2 is to obtain a plurality of a first pixel eigenvalues near a first side in the first image frame. Step 3 is to obtain a plurality of a second pixel eigenvalues near the first side in the second image frame. Step 4 is to obtain a difference eigenvalue according to the first pixel eigenvalue and the second pixel eigenvalue. Step 5 is to rotate the image frames output by the image capture unit so that the first side is corresponding to the predetermined display side if the difference eigenvalue is greater than a threshold.

The present disclosure relates to generating computer searchable text from digital images that depict documents utilizing an orientation neural network and/or text prediction neural network. For example, one or more embodiments detect digital images that depict documents, identify the orientation of the depicted documents, and generate computer searchable text from the depicted documents in the detected digital images. In particular, one or more embodiments train an orientation neural network to identify the orientation of a depicted document in a digital image. Additionally, one or more embodiments train a text prediction neural network to analyze a depicted document in a digital image to generate computer searchable text from the depicted document. By utilizing the identified orientation of the depicted document before analyzing the depicted document with a text prediction neural network, the disclosed systems can efficiently and accurately generate computer searchable text for a digital image that depicts a document.

A method and system for performing indicia recognition includes obtaining, at an image sensor, an image of an object of interest and identifying at least one region of interest in the image. The region of interest contains one or more indicia indicative of the object of interest. The processor then determines positions of each region of interest and further determines a geometric shape based on the positions of each of the regions of interest. An orientation classification is identified for each region of interest is based on a respective position relative to the geometric shape for reach region of interest. The processor then identifies and performs one or more transformations for each region of interest, with each transformation determined by each regions respective orientation classification. The processor then performs indicia recognition on each of the one or more transformed regions of interest.

Systems and methods are configured for correcting the orientation of an image data object subject to optical character recognition (OCR) by receiving an original image data object, generating initial machine readable text for the original image data object via OCR, generating an initial quality score for the initial machine readable text via machine-learning models, determining whether the initial quality score satisfies quality criteria, upon determining that the initial quality score does not satisfy the quality criteria, generating a plurality of rotated image data objects each comprising the original image data object rotated to a different rotational position, generating a rotated machine readable text data object for each of the plurality of rotated image data objects and generating a rotated quality score for each of the plurality of rotated machine readable text data objects, and determining that one of the plurality of rotated quality scores satisfies the quality criteria.

Vision-assist devices and methods are disclosed. In one embodiment, a vision-assist device includes an image sensor for generating image data corresponding to a scene, a processor, and an inertia measuring unit. The inertia measuring unit is configured to measure forces acting on the image sensor and the orientation of the image sensor. The processor is configured to receive the image data from the image sensor and process the force and orientation of the image sensor so as to determine a tilt of the image sensor. The processor is further configured to process the image data based on at least the tilt of the at least one image sensor so as to generate a corrected image data, wherein the corrected image data does not include the tilt.

Methods and systems for testing base text direction (BTD) include receiving one or more images captured by an end-user system. Each of the one or more images displays respective text test case information. Each of the one or more images is compared to a respective reference image associated with a respective text test case. It is determined whether the end-user system produces BTD errors based on the comparison in accordance with one or more BTD error rules.

A page orientation component of an image processing device receives an image of a document, transforms the image to a binarized image by performing a binarization operation on the image, and identifies a portion of the binarized image that comprises one or more rows of textual content. The page orientation component identifies a plurality of horizontal runs of white pixels and a plurality of vertical runs of white pixels in the one or more rows of textual content in the portion of the binarized image. The page orientation component generates a first histogram for the plurality of horizontal runs of white pixels, and a second histogram for the plurality of vertical runs of white pixels, and determines an orientation of the one or more rows of textual content in the image based on the first histogram and the second histogram.

Methods and associated systems for generating images with objects of interests positioned at pre-determined locations and in specific orientations (e.g., vertical to the horizon) are disclosed. The method includes generating a preview image by an image component and identifying an object of interest in the preview image. The system then determines a desirable view angle at least based on an orientation of the object of interest. The system generates an original image and a current dip angle based on a measurement performed by a tilt sensor. The system calculates an angle of rotation based on the current dip angle and the desirable view angle. The system then edits or adjusts the original image to form an edited or adjusted image that can be presented to a user.

Described are systems and methods for segmenting images which comprise impinging a substrate surface with a particle beam at each of a plurality of sensing locations which define a subset of locations within an area of interest of the substrate surface. An intensity value associated with post-impingement particles resulting from the impinging is measured and the measured intensity based on the intensity value of the sensing location is calculated. For each of a plurality of estimated locations which define a further subset of said area of interest and a corresponding estimated intensity based on at least one of the following corresponding to one or more locations proximal to the estimated location is calculated. The plurality of estimated locations is each segmented based on the corresponding estimated intensity, each of the sensing locations, and based on the corresponding measured intensity, to correspond to one of the plurality of features.