A method is provided for extracting information of interest from a measurement signal having a periodic interference pattern, which includes steps (i) of generating a filtering function representing the frequency components of the interference pattern, by implementing an analysis of an amplitude spectrum of the measurement signal based on morphological criteria, (ii) of applying the filtering function to the measurement signal so as to generate an interference signal constituted essentially by the interference pattern, and (iii) of calculating a filtered signal by carrying out a difference between the measurement signal and the interference signal.

The invention also relates to a device implementing the method.

The present disclosure provides systems, methods, and computer program products for generating a digital representation of a system from engineering documents of the system comprising one or more schematics and a components table. An example method can comprise (a) classifying, using a deep learning algorithm, (i) each of a plurality of symbols in the one or more schematics as a component and (ii) each group of related symbols as an assembly, (b) determining connections between the components and the assemblies, (c) associating a subset of the components and the assemblies with entries in the components table; and (d) generating the digital representation of the system from the components, the assemblies, the connections, and the associations. The digital representation of the system can comprise at least a digital model of the system and a machine-readable bill of materials.

A method is described to increase the efficiency of the removal of defects from document images by reorienting the conceptual framework within which an image is filtered. Rather than arbitrarily applying a filter to an entire landscape of a document image, the disclosure describes a methodology by which a document image is separated into regions of darkness and regions of light, or viewed alternatively, regions of darkness and regions of lack of darkness. Filtering is then adaptively applied to each region to remove defects.

A device for decoding magnetic patterns printed on documents comprising a reading head (12) having: a reader (20) arranged to read first magnetic signals belonging to the magnetic patterns and to electromagnetic noise due to sources internal and/or external to the device. The device further comprises: a further reader (40), arranged to read second magnetic signals belonging to the electromagnetic noise, an adder component (25) arranged to algebraically subtract the amplified second magnetic signals from the amplified first magnetic signals, and a converter (16) arranged to convert the resulting signal into a digital signal representing the read magnetic patterns. A method for decoding magnetic patterns is also disclosed.

A recognition system for recognizing multiple inputs of gestures, handwriting symbols and virtual keys on a touch screen includes a touch IC serves to convert a plurality of touch signals of the touch screen to a touch data frame. A processor set is connected to the touch IC and serves to perform a touch data processing on the touch data frame. The touch data processing is performed by using a processing directly executed by an OS (Operating System) and a processing of AI (artificial intelligence) recognizing. An AI recognition module is connected to the processor set. The AI recognition module is used for recognizing multiple key inputs, operation gestures and handwriting symbols. The key inputs and handwriting symbols are corrected by a grammar correction and a symbol correction respectively. The touch screen serves to display a virtual keyboard.

A recognition system for recognizing multiple inputs of gestures, handwriting symbols and virtual keys on a touch screen includes a touch IC serves to convert a plurality of touch signals of the touch screen to a touch data frame. A processor set is connected to the touch IC and serves to perform a touch data processing on the touch data frame. The touch data processing is performed by using a processing directly executed by an OS (Operating System) and a processing of AI (artificial intelligence) recognizing. An AI recognition module is connected to the processor set. The AI recognition module is used for recognizing multiple key inputs, operation gestures and handwriting symbols. The key inputs and handwriting symbols are corrected by a grammar correction and a symbol correction respectively. The touch screen serves to display a virtual keyboard.

An image processing apparatus that is capable of restoring a degraded image with high accuracy The image processing apparatus acquires image data including an image of character information and identifies a character type of the character information. A learned model adapted to the identified character type is acquired from a plurality of learned models subjected to machine learning using images for learning, which are associated with a plurality of character type conditions, respectively, and correct answer images associated therewith. The image of character information is input to the acquired learned model to restore the image of character information.

An image processing apparatus that is capable of restoring a degraded image with high accuracy The image processing apparatus acquires image data including an image of character information and identifies a character type of the character information. A learned model adapted to the identified character type is acquired from a plurality of learned models subjected to machine learning using images for learning, which are associated with a plurality of character type conditions, respectively, and correct answer images associated therewith. The image of character information is input to the acquired learned model to restore the image of character information.

A document management system performs optical character recognition (OCR) on a high resolution version of an image of a document, obtaining a first set of text representative of the text of the document. The document management system applies a machine-learned model on a low-resolution version of the image of the document, producing a denoised image that is of a higher resolution than that input into the machine-learned model. The document management system performs OCR on the denoised image, obtaining a second set of text representative of the text of the document. The document management system compares the first and second sets of text in the form of an objective loss function. The document management system subsequently generates a feature vector from the comparison of the sets of text and retrains the machine-learned model with the generated feature vector.

A document management system performs optical character recognition (OCR) on a high resolution version of an image of a document, obtaining a first set of text representative of the text of the document. The document management system applies a machine-learned model on a low-resolution version of the image of the document, producing a denoised image that is of a higher resolution than that input into the machine-learned model. The document management system performs OCR on the denoised image, obtaining a second set of text representative of the text of the document. The document management system compares the first and second sets of text in the form of an objective loss function. The document management system subsequently generates a feature vector from the comparison of the sets of text and retrains the machine-learned model with the generated feature vector.