A computer-implemented method for recognizing handwritten text in user input applied onto a touch-based user interface, comprising: receiving data representing the user input, the data comprising a sequence of ink points; applying the received data to a segmentation-based handwriting recognition by: - segmenting the sequence of ink points into a plurality of segments; - generating a character hypothesis as one or more consecutive segments of the plurality of segments; and - applying the character hypothesis to a character classifier to obtain a first probability corresponding to the probability that the character hypothesis includes a given character of a pre-defined alphabet; applying the received data to a neural network-based sequence classifier trained with a Connectionist Temporal Classification output layer using forced alignment to generate an output, wherein the output of the CTC output layer comprises, for said given character, the probabilities of observing the given character at each ink point of the sequence of ink points; processing the output of the CTC output layer to determine a second probability corresponding to the probability that the given character is observed within said character hypothesis used by the segmentation-based handwriting recognition, wherein said processing comprises: - filtering the output of the CTC output layer based on the character hypothesis adopted by the segmentation-based handwriting recognition, wherein said filtering comprises extracting from the output of the CTC output layer a set of probabilities corresponding to the probabilities of observing the given character, for any given alphabet character or blank, and for any ink point of the sequence of ink points falling in the character hypothesis; and - decoding the filtered output to obtain the second probability; 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. receiving data representing the user input, the data comprising a sequence of ink points; applying the received data to a segmentation-based handwriting recognition by: - segmenting the sequence of ink points into a plurality of segments; - generating a character hypothesis as one or more consecutive segments of the plurality of segments; and - applying the character hypothesis to a character classifier to obtain a first probability corresponding to the probability that the character hypothesis includes a given character of a pre-defined alphabet; - segmenting the sequence of ink points into a plurality of segments; - generating a character hypothesis as one or more consecutive segments of the plurality of segments; and - applying the character hypothesis to a character classifier to obtain a first probability corresponding to the probability that the character hypothesis includes a given character of a pre-defined alphabet; applying the received data to a neural network-based sequence classifier trained with a Connectionist Temporal Classification output layer using forced alignment to generate an output, wherein the output of the CTC output layer comprises, for said given character, the probabilities of observing the given character at each ink point of the sequence of ink points; processing the output of the CTC output layer to determine a second probability corresponding to the probability that the given character is observed within said character hypothesis used by the segmentation-based handwriting recognition, wherein said processing comprises: - filtering the output of the CTC output layer based on the character hypothesis adopted by the segmentation-based handwriting recognition, wherein said filtering comprises extracting from the output of the CTC output layer a set of probabilities corresponding to the probabilities of observing the given character, for any given alphabet character or blank, and for any ink point of the sequence of ink points falling in the character hypothesis; and - decoding the filtered output to obtain the second probability; and - filtering the output of the CTC output layer based on the character hypothesis adopted by the segmentation-based handwriting recognition, wherein said filtering comprises extracting from the output of the CTC output layer a set of probabilities corresponding to the probabilities of observing the given character, for any given alphabet character or blank, and for any ink point of the sequence of ink points falling in the character hypothesis; and - decoding the filtered output to obtain the second probability; 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.