Ink-processing technology is set forth herein for detecting a gesture that a user performs in the course of interacting with an ink document. The technology operates by identifying a grouping of ink strokes created by the user. The technology then determines whether the grouping expresses a gesture based on a combination of spatial information and image information, both of which describe the grouping. That is, the spatial information describes a sequence of positions traversed by the user in drawing the grouping of ink strokes using an ink capture device, while the image information refers to image content in an image produced by rendering the grouping into image form. The technology also provides a technique for identifying the grouping by successively expanding a region of analysis, to ultimately provide a spatial cluster of ink strokes for analysis.

A system, method, and computer program product embodiment related to a digital paper-based interaction to system data. An embodiment operates by receiving a written instruction from a user; analyzing the written instruction to determine a user intent and one or more parameters; retrieving a set of data from a data application; rendering a representation of the set of data in the user experience; receiving a second written instruction from the user in response to a condition in the representation of the set of data; deriving a conclusion based on the written instruction and the second written instruction; building a flow comprising a sequence of actions undertaken, by the user experience, in response to a user action; and adding the flow to a knowledge base.

A communication system captures writing surface content in a physical space for transmittal to remote client devices participating in a communication session. During a communication session with one or more remote client devices, a communication system captures image data and depth data describing objects in a physical space of the communication system. Based on the captured data, the communication system identifies a writing surface in the physical space and captures content on the writing surface. The communication system may also identify objects occluding content on the writing surface based on the captured data and may modify image data to make an object occluding the content at least partially transparent. The communication system transmits the content to at least one of the remote client devices participating in the communication session.

A system for machine learning that is configured to receive an input having a plurality of features and predict one or more attributes of the input. The system includes a security mechanism, which determines an initial value for each of the features; determines a perturbation value for each of the features, the perturbation being randomly selected; adds the perturbation value to the initial value to determine a perturbed value for each of the features; and quantizes the perturbation value for each of the features to determine a quantized value for each of the features. The system also includes a classifier that receives the quantized value for each of the features and predict the one or more attributes of the input based on the quantized value for each of the features.

A handwriting recognition method is provided, which includes: obtaining handwritten original trajectory data in real-time; compressing the handwritten original trajectory data, to obtain compressed handwritten trajectory data; and inputting the compressed handwritten trajectory data into a compressed handwriting recognition model for recognition, to obtain a text recognition result corresponding to the handwritten original trajectory data. A handwriting recognition model is obtained by training with handwritten trajectory data of each piece of training data in a training data set, and the compressed handwriting recognition model is obtained by performing model compression on the handwriting recognition model. The handwriting recognition method can address the problem in the related art that the handwriting recognition accuracy is low caused by incorrect segmentation, thereby effectively improving the handwriting recognition accuracy.

A computer-implemented method for automatically analyzing handwritten text to determine a mismatch between a purported writer and an actual writer is disclosed. The method comprises receiving two samples of digitized handwriting each allegedly created by one individual and received and entered into a digital system by another. The method further comprises performing a series of feature extractions to convert the samples into two vectors of extracted features; automatically clustering a set of vectors such that the first vector and the second vector are assigned to the same cluster among multiple clusters, based on vector similarity; and automatically determining that a same individual being associated with both the first and second samples indicates a heightened probability that the individual fraudulently created both samples. Finally, the method comprises automatically transmitting a message to flag additional samples of digitized handwriting entered into a digital system as possibly fraudulent.

Systems and methods relate generally to attendee authentication. In a method, a robot gatekeeper has a multi-function printer with program code configured for character recognition and handwriting analysis. The program code is executed by a processor coupled to the memory to initiate operations including: instructing for placement of a hand for a palm vein scanner and a badge for a badge reader; reading a badge to obtain first identification information; reading a palm to obtain first biometric data; accessing a database to obtain second identification information responsive to the first identification information; comparing the first biometric data and second biometric data obtained from the second identification information; printing an anti-tampering feature on a card; scanning a hand written sample on the card; and analyzing the hand written sample scanned with respect to at least one handwriting exemplar in or associated with the second identification information.

A method of identifying and recognizing characters using a dual-stage neural network pipeline, the method including: receiving, by a computing device, image data; providing the image data to a first convolutional layer of a convolutional neural network (CNN); applying, using the CNN, pattern recognition to the image data to identify a region of the image data containing text; providing sub-image data comprising the identified region of the image data to a convolutional recurrent neural network (CRNN); and recognizing, using the CRNN, the characters within the sub-image data.

Methods, systems, and computer-readable media related to a technique for providing handwriting input functionality on a user device. A handwriting recognition module is trained to have a repertoire comprising multiple non-overlapping scripts and capable of recognizing tens of thousands of characters using a single handwriting recognition model. The handwriting input module provides real-time, stroke-order and stroke-direction independent handwriting recognition for multi-character handwriting input. In particular, real-time, stroke-order and stroke-direction independent handwriting recognition is provided for multi-character, or sentence level Chinese handwriting recognition. User interfaces for providing the handwriting input functionality are also disclosed.

A display apparatus includes circuitry to accept input of handwriting data, perform recognition processing of the handwriting data, and control frequency of the recognition processing in accordance with a power supply state of the display apparatus.