An information processing apparatus includes a processor configured to acquire a result of character recognition of a character string formed on a medium and read by scanning that is subject to character recognition and replace a character or a symbol in a subject with a reference character string that is referred to by the character or the symbol.

An information processing apparatus includes a processor configured to acquire a result of character recognition of a character string formed on a medium and read by scanning that is subject to character recognition and replace a character or a symbol in a subject with a reference character string that is referred to by the character or the symbol.

An object recognition device including an artificial neural network (NN) engine configured to receive learning data and weights, make an object recognition model (ORM) learn by using the received information, and provide selected weight data including weights from the selected portion of the weights, and further configured to receive a feature vector, and apply the feature vector extracted from an object data that constructs the object and the selected weight data to the learned ORM to provide an object recognition result, a nonvolatile memory (NVM) configured to store the learned ORM, and an error correction code (ECC) engine configured to perform an ECC encoding on the selected weight data to generate parity data, provide the selected weight data and the parity data to the NVM, and provide the selected weight data to the NN engine by performing an ECC decoding on the selected weight data based on the parity data.

A computer-implemented method for generating feedback based on a handwritten text, comprises the steps of initializing a writing instrument to be used in a writing operation comprising a handwritten text and capturing and processing the handwritten text to generate digital text data. The method further comprises the steps of identifying at least one handwritten text attribute associated with the digital text data, comparing the at least one handwritten text attribute with predefined textual feature attributes, and generating a textual feature based on the compared at least one handwritten text attribute and predefined textual feature attributes. In addition, the method comprises the steps of modifying the digital text data using the textual feature and generating feedback to a user based on the modified digital text data.

A computer-implemented method for generating feedback based on a handwritten text, comprises the steps of initializing a writing instrument to be used in a writing operation comprising a handwritten text and capturing and processing the handwritten text to generate digital text data. The method further comprises the steps of identifying at least one handwritten text attribute associated with the digital text data, comparing the at least one handwritten text attribute with predefined textual feature attributes, and generating a textual feature based on the compared at least one handwritten text attribute and predefined textual feature attributes. In addition, the method comprises the steps of modifying the digital text data using the textual feature and generating feedback to a user based on the modified digital text data.

Disclosed is an electronic device. An electronic device comprises: a display provided at a front surface of the electronic device; a storage; a communication interface including a circuit; and a processor for providing a board screen on the display, identifying a display size of an image on the basis of resolution information of the image when the image is received through the communication interface, adding the image onto the board screen on the basis of the identified display size, adjusting at least one of a display location or a display size of the image to correspond to a first touch input when the first touch input with respect to the image is detected, and adding a drawing object corresponding to a second touch input onto the board screen when the second touch input with respect to the board screen is detected.

A method may include obtaining an image that includes a first graphics element and a second graphics element, determining that the first graphics element corresponds to a command and that the second graphics element is a non-command, and generating an electronic document by executing the command. The electronic document may include a revised version of the second graphics element, but not the first graphics element. The electronic document may be generated in response to the first graphics element corresponding to the command.

Disclosed is an electronic device. An electronic device comprises: a display provided at a front surface of the electronic device; a storage; a communication interface including a circuit; and a processor for providing a board screen on the display, identifying a display size of an image on the basis of resolution information of the image when the image is received through the communication interface, adding the image onto the board screen on the basis of the identified display size, adjusting at least one of a display location or a display size of the image to correspond to a first touch input when the first touch input with respect to the image is detected, and adding a drawing object corresponding to a second touch input onto the board screen when the second touch input with respect to the board screen is detected.

A method includes providing a robot, providing an image of drawn handwritten characters to the robot, enabling the robot to capture a bitmapped image of the image of drawn handwritten characters, enabling the robot to infer a plan to replicate the image with a writing utensil, and enabling the robot to reproduce the image.

A computer-implemented method, a computing system, and a computer program product, for automatically labeling an amount of unlabeled data for training one or more classifiers of a machine learning system. A method includes iteratively processing unlabeled data items. Receiving an unlabeled data item into each autoencoder in an autoencoder architecture. Each autoencoder processing with a lowest loss of information the unlabeled data item that is likely associated with a label associated with the autoencoder, while processing with a higher loss of information the unlabeled data item that is likely not associated with the label. Predicting, based on loss of information, a probability distribution for the unlabeled data item. Automatically associating the label to the unlabeled data item, based on the label being associated with a highest probability in a peaking probability distribution associated with the unlabeled data item. The autoencoder architecture can include a cloud computing network architecture.