A method of providing a handwriting style correction function and an electronic device adapted to the method are provided. The electronic device includes: a touch screen; a processor electrically connected to the touch screen; and a memory electrically connected to the processor. The memory stores instructions which, when executed by the processor, cause the processor to perform operations comprising: displaying at least one reference character on the touch screen; receiving a touch gesture via the touch screen; displaying a track of the received touch gesture on the touch screen; recognizing the track of the touch gesture as at least one input character corresponding to at least one reference character; identifying at least one reference character and at least one input character, as at least one stroke, based on a preset standard; comparing corresponding strokes of at least one reference character with at least one input character, and determining errors by strokes; and summing the errors by strokes of each of at least one reference character, and determining errors by characters.

The present disclosure is generally directed to systems and methods for executing optical character recognition faster than at least some traditional OCR systems, without sacrificing recognition accuracy. Towards this end, various exemplary embodiments involve the use of a bounding box and a grid-based template to identify certain unique aspects of each of various characters and/or numerals. For example, in one embodiment, the grid-based template can be used to recognize a numeral and/or a character based on a difference in centerline height between the numeral and the character when a monospaced font is used. In another exemplary embodiment, the grid-based template can be used to recognize an individual digit among a plurality of digits based on certain parts of the individual digit being uniquely located in specific portions of the grid-based template.

The invention provides a method for CSI-based fine-grained gesture recognition, wherein the method comprises the following steps: determining a start point, an end point, a velocity, a direction and/or an inflection point of at least one stroke gesture in multiple dimensions according to an eigenvalue of channel state information; dividing the strokes according to the start point, the end point, the velocity, the direction and/or the inflection point of the stroke using a machine learning method and forming a stroke sequence; building a stroke decipherment model according to frequencies of the strokes appearing in natural language rules and/or scientific language rules and/or connection rules between the strokes; and dividing and recognizing the stroke sequence as a letter sequence, a radical sequence, a numeral sequence and/or a pattern sequence conforming to the natural language rules and/or the scientific language rules using the stroke decipherment model. The present invention involves recognizing strokes of characters from finger gesture, and then recovering characters from the strokes, so as to enrich types of languages that can be recognized from finger gesture and enhance recognition accuracy of gesture writing.

A computer-implemented technique is described herein that receives captured stroke information when a user enters handwritten notes using an input capture device. The technique then automatically performs analysis on the captured stroke information to produce output analysis information. Based on the output analysis information, the technique uses an assistant component to identify a response to the captured stroke information and/or to identify an action to be performed. The technique then presents the response, together with the original captured stroke information. In addition, or alternatively, the technique performs the action. In one case, the response is a text-based response; that text-based response may be presented in a freeform handwriting style to give the user the impression that a virtual assistant is responding to the user's own note. In another case, the response engages the user in an interactive exercise of any type.

A method for character recognition. The method includes: obtaining a plurality of character segments extracted from an image; determining a first character bounding box including a first set of the plurality of character segments and a second character bounding box including a second set of the plurality of character segments; determining an ordering for the first set based on a plurality of texture properties for the first set; determining a plurality of directions of the first set based on a plurality of brush widths and a plurality of intensities for the first set; and executing character recognition for the first character bounding box by sending the first set, the plurality of directions for the first set, and the ordering for the first set to an intelligent character recognition (ICR) engine.

A method for processing an image with a processor, including: identifying, within an image, a candidate arrow including a long path adjacent to a plurality of short paths; determining a direction of the candidate arrow based on the long path; estimating a tip pixel within the plurality of short paths based on the direction of the candidate arrow; determining, for the candidate arrow, a plurality of arrowhead edges in the plurality of short paths based on the tip pixel; and generating, for the candidate arrow, a plurality of refined arrowhead edges based on the plurality of arrowhead edges, the direction, and the tip pixel.

A method and a system for digitization of a document are disclosed. The document is scanned to generate an electronic document. One or more characters in a first set of portions of the electronic document are identified, based on a character recognition technique. Each portion in the first set of portions is classified in one or more groups based on at least a status of identification of the corresponding one or more characters. Further, one or more tasks are created for each of the one or more groups. The one or more tasks are transmitted to one or more crowdworkers, based at least on the respective type of the one or more tasks. Further, a response for each of the one or more tasks is received. Based on the received response, a digitized document is generated.

An improved machine learning system is provided. For example, a content management server may provide a digital assessment of a user's handwriting to assess the user's knowledge of a language. The assessment may comprise adaptive technology to help determine initial questions to provide to the user as well as follow-up questions to clarify appropriate remediation content in a particular context. The content management server may also provide real-time analysis, including assessing multiple users at the same time in adjusting the assessment based on the digital input from each of these users. In some examples, the content management server may incorporate handwriting analysis methods to perform object detection and score handwriting input.

An improved machine learning system is provided. For example, a content management server may provide real-time analysis of a user's handwriting to assess the user's knowledge of a language, including using a convolution neural network method.

An improved machine learning system is provided. For example, a content management server may provide real-time analysis of a user's handwriting to assess the user's knowledge of a language, including using a convolution neural network method.