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.

Methods and systems are provided for end-to-end text recognition in digitized documents of handwritten characters over multiple lines without explicit line segmentation. An image is received. Based on the image, one or more feature maps are determined. Each of the one or more feature maps include one or more feature vectors. Based at least in part on the one or more feature maps, one or more scalar scores are determined. Based on the one or more scalar scores, one or more attention weights are determined. By applying the one or more attention weights to each of the one or more feature vectors, one or more image summary vectors are determined. Based at least in part on the one or more image summary vectors, one or more handwritten characters are determined.

Multiple search patterns may be obtained. Characters within the multiple search patterns may be included within multiple alphabets. A pool including the characters within the multiple search patterns may be defined. A pointer for text to be searched may be set. Whether a character of the text corresponding to the pointer matches any character within the pool may be determined. Based on the character of the text corresponding to the pointer matching any character within the pool, a first portion of the text may be selected for a search of the multiple search patterns. Based on the character of the text corresponding to the pointer not matching any character within the pool, a second portion of the text may be skipped from the search of the multiple search patterns.

One aspect of the present disclosure relates to a method of sentiment analysis based on ambiguity analysis, which includes analyzing information with the sentiment analysis models and the ambiguity analysis models. Another aspect of the present disclosure relates to a method of training the sentiment analysis models and ambiguity analysis models, which includes acquiring information, constructing lexicons, conducting sentiment analysis and ambiguity analysis with said lexicons, acquiring corpus, and training models, etc. Meanwhile, another aspect of the present disclosure relates to a system of sentiment analysis, which includes input, and output modules, acquisition modules, processing modules and database.

An interaction method, an electronic device and a computer storage medium. The interaction method includes: acquiring a trajectory point set input by a user, wherein the trajectory point set at least includes one trajectory point; determining a candidate object according to the trajectory point set, wherein the candidate object includes a first candidate object and a second candidate object; displaying a first selection icon for representing the first candidate object and a second selection icon for representing the second candidate object; and displaying the first candidate object or executing an operation corresponding to the first candidate object in response to the user's selection operation on the first selection icon, and displaying the second candidate object or executing an operation corresponding to the second candidate object in response to the user's selection operation on the second selection icon.

A system and method for correcting image data is disclosed. The method includes receiving one or more documents from one or more electronic mediums. The method further includes determining a primary character and one or more alternate characters corresponding to the mis-captured character image, extracting one or more confident instances of the primary character and the one or more alternate characters from the one or more documents and generating one or more scores corresponding to the primary character and the one or more alternate characters. Further, the method includes predicting a correct character corresponding to the mis-captured character image by using a trained image prediction-based ML model and automatically replacing the mis-captured character image with the predicted correct character.

One aspect of the present disclosure relates to a method of sentiment analysis based on ambiguity analysis, which includes analyzing information with the sentiment analysis models and the ambiguity analysis models. Another aspect of the present disclosure relates to a method of training the sentiment analysis models and ambiguity analysis models, which includes acquiring information, constructing lexicons, conducting sentiment analysis and ambiguity analysis with said lexicons, acquiring corpus, and training models, etc. Meanwhile, another aspect of the present disclosure relates to a system of sentiment analysis, which includes input, and output modules, acquisition modules, processing modules and database.

A book digitization apparatus includes: a three-dimensional data generation unit that generates three-dimensional data of a book; a two-dimensional page data generation unit that generates two-dimensional page data from the three-dimensional data; and a character recognition unit that extracts a plurality of unique points of a character from a plurality of points which are included in the two-dimensional page data and each of which has a value corresponding to ink, thereby recognizing the character.

A computer-implemented technique is described herein that receives captured stroke information when a user enters a handwritten note using an input capture device. The technique then analyzes the captured stroke information to produce output analysis information. Based on the output analysis information, the technique modifies the captured stroke information into an actionable form that contains one or more actionable content items, while otherwise preserving the original form of the captured stroke information. The technique then presents the modified stroke information on a canvas display device. The user may subsequently activate one or more actionable content items in the modified stroke information to perform various supplemental tasks that pertain to the handwritten note. In one case, for example, the technique can recognize the presence of entity items and/or list items in the note and then reproduce them in an actionable form.

Advanced signal processing technology including signal encoding and digital watermarking. Disclosure includes monitoring spectral differences within control strip area to determine signal robustness of encoded signals printed outside of control strip area. Other combinations and technology provided too.