Myscript

Cyril Cerovic

Khaoula Elagouni

Robin Mélinand

Romain Bednarowicz

Fabien Ric

Claire Sidoli

Anne Bonnaud

Nicolas Rucine

Fabio Valente

Gregory Cousin

Erwan Jestin

Anthony Laurence

David Hebert

Maël Nagot

Pierre-Michel Lallican

Guillermo Aradilla

Julien Vergne

Gaël Manson

A method for recognizing gesture strokes in user input, comprising: receiving data generated based on the user input, the data representing a stroke and comprising a plurality of ink points in a rectangular coordinate space and a plurality of timestamps associated respectively with the plurality of ink points; segmenting the plurality of ink points into a plurality of segments each corresponding to a respective sub-stroke of the stroke and comprising a respective subset of the plurality of ink points; generating a plurality of feature vectors based respectively on the plurality of segments; and applying the plurality of feature vectors as an input sequence representing the stroke to a trained stroke classifier to generate a vector of probabilities including a probability that the stroke is a non-gesture stroke and a probability that the stroke is a given gesture stroke of a set of gesture strokes.

Gesture stroke recognition in touch-based user interface input

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 guiding hand-drawing of diagrams including text and non-text elements on a computing device are provided. The computing device has a processor and a non-transitory computer readable medium for detecting and recognizing hand-drawing diagram element input under control of the processor. Display is performed, on an interactive display of the computing device, of a guide element associated with at least one diagram element of displayed handwriting diagram input. The guide element is configured with a depiction of the at least one diagram element in recognized form.

System and method of guiding handwriting diagram input

A system, method and computer program product for hand-drawing diagrams including text and non-text elements on a computing device are provided. The computing device has a processor and a non-transitory computer readable medium for detecting and recognizing hand-drawing diagram element input under control of the processor. Display of input diagram elements in interactive digital ink is performed on a display device associated with the computing device. One or more of the diagram elements are associated with one or more other of the diagram elements in accordance with a class and type of each diagram element. The diagram elements are re-displayed based on one or more interactions with the digital ink received and in accordance with the one or more associations.

System and method of handwriting recognition in diagrams

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Nantes, France

Cyril Cerovic