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.

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.

Disclosed is a system for performing User Interface (UI) verification of a Device Under Test (DUT). Before performing the UI verification, a set of corner markers is positioned at corners of a display frame associated to the DUT. Once the set of corner markers are positioned, an image receiving module receives a DUT image, captured by an image capturing unit, of the UI pertaining to a DUT. A skew correction module for correcting orientation of the DUT image by determining an orientation correction factor. A file configuration module for storing the orientation correction factor in a pre-configuration file when the DUT image is occupying the content greater than the predefined threshold percentage. In one aspect, the orientation correction factor may be referred while testing a UI of the DUT.

An object is to easily and appropriately identify the orientation of imaging means at the time of image capturing. A control section of an imaging device or an image processing device acquires an image captured by an imaging section and performs image recognition processing of recognizing a photographic subject corresponding to a first orientation such as a horizontal imaging orientation or a vertical imaging orientation in the image so as to judge whether a predetermined photographic subject is present in the image. Then, based on the judgment result, the control section identifies whether the orientation of the imaging device or the imaging section at the time of image capturing is the first orientation or a second orientation.

An example apparatus including memory to store a first image of a document and a second image of the document, and a processor coupled to the memory, wherein the processor is to: perform optical character recognition on the first image to generate a first output dataset; perform optical character recognition on the second image to generate a second output dataset; and compute a transformation matrix based on the first output dataset and the second output dataset, the transformation matrix to align the first image with the second image.

Provided are character image processing methods and apparatuses, devices, storage medium, and computer programs. The character image processing method mainly comprises: obtaining at least one image block containing a character in a character image to be processed; obtaining image block form transformation information of the image block on the basis of a neural network, the image block form transformation information being used for changing a character orientation in the image block to a predetermined orientation, and the neural network being obtained by means of training using an image block sample having form transformation label information; performing form transformation processing on the character image to be processed according to the image block form transformation information; and performing character recognition on the character image to be processed which is subjected to the form transformation.

An intelligent TTS providing method and an intelligent computing device providing TTS are disclosed. An intelligent TTS providing method according to an embodiment of the present disclosure can seamlessly provide continuous TTS by receiving a text read command, adjusting a photographing angle of a camera such that a position of an object on which text is written is included in the photographing angle, photographing the object, converting the text written on the object into a speech and outputting the speech. One or more of the intelligent computing device and artificial intelligent speaker of the present disclosure can be associated with artificial intelligence (AI) modules, unmanned aerial vehicle (UAV) robots, augmented reality (AR) devices, virtual reality (VR) devices, 5G service related devices, etc.

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 computer-implemented method (300) for extracting content (302) from a physical writing surface (304), the method (300) comprising the steps of: (a) receiving a reference frame (306) including image data relating to at least a portion of the physical writing surface (304), the image data including a set of data points; (b) determining an extraction region (308), the extraction region (308) including a subset of the set of data points from which content (302) is to be extracted; (c) extracting content (302) from the extraction region (308) and writing the content (302) to a display frame (394); (d) receiving a subsequent frame (406) including subsequent image data relating to at least a portion of the physical writing surface (304), the subsequent image data including a subsequent set of data points; (e) determining a subsequent extraction region (408), the subsequent extraction region (408) including a subset of the subsequent set of data points from which content (402) is to be extracted; and (f) extracting subsequent content (402) from the subsequent extraction region (408) and writing the subsequent content (402) to the display frame (394).

Described are approaches for assigning tasks between machine resources (e.g., AI task performers, AI task validators), human resources (e.g., task performers, task validators), and/or other smart systems to facilitate collaborative text detection, text recognition, and text retrieval in order to optimize system performance along a variety of different selection criteria specifying various performant dimensions, including, but not limited to improving system efficiency, reducing task performer and/or task validator idle time, improving triage outcomes, reducing data processing loads, maintaining client confidentiality, etc., that may be associated with one or more customers.