Myscript

Nuance Communications, Inc.

Veovox Sa

Guillermo Aradilla

Pierre-Michel Lallican

Zsolt Wimmer

Rainer Gruhn

Freddy Perraud

Gaël Manson

Cyril Cerovic

Jorge Carmona

John Dines

Olivier Masson

Daniel Vasquez

Daniel Andrés Vásquez Cano

A method for recognizing handwritten text is disclosed. The method comprises receiving data comprising a sequence of ink points; applying the received data to a neural network-based sequence classifier trained with a Connectionist Temporal Classification (CTC) output layer using forced alignment to generate an output; generating a character hypothesis as a portion of the sequence of ink points; applying the character hypothesis to a character classifier to obtain a first probability corresponding to the probability that the character hypothesis includes the given character; processing the output of the CTC output layer to determine a second probability corresponding to the probability that the given character is observed within the character hypothesis; and combining the first probability and the second probability to obtain a combined probability corresponding to the probability that the character hypothesis includes the given character.

Recognizing handwritten text by combining neural networks

A system, method and computer program product for use in recognizing content associated with handwritten stroke input to a computing device is provided. The computing device is connected to an input interface. A user is able to provide input by applying pressure to or gesturing above the input interface using a finger or an instrument such as a stylus or pen. The computing device has an input management system for recognizing content defined by the input. The input management system is configured to detect input of a handwritten stroke with respect to the interactive key layout, characterize the detected handwritten stroke by a reference stroke by determining a sequence of reference points associated with a sequence of interactive keys of the interactive key layout, assign probability scores to candidate characters, and cause recognition of sequences of characters by applying a language model in accordance with the assigned probability scores.

System and method for recognizing handwritten stroke input

A system and method that is able to recognize a user's natural superimposed handwriting without any explicit separation between characters. The system and method is able to process single-stroke and multi-stroke characters. It can also process cursive handwriting. Further, the method and system can determine the boundaries of input words either by the use of a specific user input gesture or by detecting the word boundaries based on language characteristics and properties. The system and method analyzes the handwriting input through the processes of fragmentation, segmentation, character recognition, and language modeling. At least some of these processes occur concurrently through the use of dynamic programming.

System and method for superimposed handwriting recognition technology

An approach for phoneme recognition is described. A sequence of intermediate output posterior vectors is generated from an input sequence of cepstral features using a first layer perceptron. The intermediate output posterior vectors are then downsampled to form a reduced input set of intermediate posterior vectors for a second layer perceptron. A sequence of final posterior vectors is generated from the reduced input set of intermediate posterior vectors using the second layer perceptron. Then the final posterior vectors are decoded to determine an output recognized phoneme sequence representative of the input sequence of cepstral features.

Downsampling schemes in a hierarchical neural network structure for phoneme recognition

The invention provides a method for automated training of a plurality of artificial neural networks for phoneme recognition using training data, wherein the training data comprises speech signals subdivided into frames, each frame associated with a phoneme label, wherein the phoneme label indicates a phoneme associated with the frame. A sequence of frames from the training data are provided, wherein the number of frames in the sequence of frames is at least equal to the number of artificial neural networks. Each of the artificial neural networks is assigned a different subsequence of the provided sequence, wherein each subsequence comprises a predetermined number of frames. A common phoneme label for the sequence of frames is determined based on the phoneme labels of one or more frames of one or more subsequences of the provided sequence. Each artificial neural network using the common phoneme label.

Method for automated training of a plurality of artificial neural networks

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Nantes, France

Guillermo Aradilla