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

Managing real-time handwriting recognition

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. User interfaces for providing the handwriting input functionality are also disclosed.

Multi-script handwriting recognition using a universal recognizer

Pattern recognition of time-series signals for movement and gesture analysis plays an important role in many fields as diverse as healthcare, astronomy, industry and entertainment. As a new technique in recent years, Deep Learning (DL) has made tremendous progress in computer vision and Natural Language Processing (NLP), but largely unexplored on its performance for movement and gesture recognition from noisy multi-channel sensor signals. To tackle this problem, this study was undertaken to classify diverse movements and gestures using four developed DL models: a 1-D Convolutional neural network (1-D CNN), a Recurrent neural network model with Long Short Term Memory (LSTM), a basic hybrid model containing one convolutional layer and one recurrent layer (C-RNN), and an advanced hybrid model containing three convolutional layers and three recurrent layers (3+3 C-RNN). The models will be applied on three different databases (DB) where the performances of models were compared. DB1 is the HCL dataset which includes 6 human daily activities of 30 subjects based on accelerometer and gyroscope signals. DB2 and DB3 are both based on the surface electromyography (sEMG) signal for 17 diverse movements. The evaluation and discussion for the improvements and limitations of the models were made according to the result.

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Movement and Gesture Recognition Using Deep Learning and Wearable-sensor Technology

The present disclosure generally relates to handwriting on touch sensitive surfaces. In some examples, text suggestions strokes entered on a touch sensitive surface are viewed and selected in response to a rotatable input mechanism. In some examples, text determined from a set of strokes on the touch sensitive surface is revised based on a subsequently entered stroke on the touch sensitive surface. In some examples, a determination is made whether to include a stroke in a set of strokes based a time between the stroke and the previous stroke. In some examples, determining text based on a set of stroke is interrupted to determined revised text based on the set of strokes and a second stroke.

Handwriting keyboard for screens

Deep learning is the domain of machine learning that implements deep neural architectures, with multiple hidden layers to mimic the functions of the human brain. The network learns from multiple levels of representation and accordingly responds to different levels of abstraction, where each layer learns different patterns. Handwritten digit recognition is a classic machine learning problem to evaluate the performance of classification algorithms. This paper focuses on the implementation of deep neural networks and deep learning algorithms. The NN algorithms such as DNN, CNN, and RNN are implemented for the classification of handwritten digits. The algorithms are implemented on various deep learning frameworks and the performance is evaluated in terms of accuracy of the models. The best accuracy is of CNN 99.6% model and the error rate of algorithms ranges from 0.2–3%.

Recognition of Handwritten Digits Using DNN, CNN, and RNN

This paper presents a library written by C# language for the online handwriting recognition system using UNIPEN-online handwritten training set. The recognition engine based on convolution neural networks and yields recognition rates to 99% to MNIST training set, 97% to UNIPEN's digit training set (1a), 89% to a collection of 44022 capital letters and digits (1a,1b) and 89% to lower case letters (1c). These networks are combined to create a larger system which can recognize 62 English characters and digits. A proposed handwriting segmentation algorithm is carried out which can extract sentences, words and characters from handwritten text. The characters then are given as the input to the network.

Dũng Viṿt Phạm

Papers

Online handwriting recognition using multi convolution neural networks