Handwriting verification methods and related computer systems, and handwriting-based user authentication methods and related computer systems are disclosed. A handwriting verification method comprises obtaining a handwriting test sample containing a plurality of available parameters, extracting geometric parameters, deriving geometric features comprising an x-position value and a y-position value for each of a plurality of feature points in the test sample, performing feature matching between geometric features of the test sample and a reference sample, determining a handwriting verification result based at least in part on the feature matching, and outputting the handwriting verification result. Techniques and tools for generating and preserving electronic handwriting data also are disclosed. Raw handwriting data is converted to a streamed format that preserves the original content of the raw handwriting data. Techniques and tools for inserting electronic handwriting data into a digital image also are disclosed.

An input display device includes a display unit that displays a screen for handwriting input on an input screen including a plurality of input fields, a stroke data processing unit that groups stroke data which is input to a capture screen by handwriting into stroke data representing characters to generate grouped stroke data as grouping stroke data, a character recognition unit that conducts character recognition on the grouping stroke data to convert the grouping stroke data into at least one recognized character, and a control processing unit that displays the at least one recognized character at the plurality of input fields of the input screen correlated with positions, at which handwriting input was performed, in the screen for handwriting input.

A handwriting text recognition system based on neural network includes a stroke input processor for receiving tracks of online handwriting texts, a string database for storing a large amount of the tracks; a word recognition neural network; and an after-processor being connected to the string database and the output interface of the text recognition neural network; The handwriting text recognition system based on neural network provides higher rates of confidences. Some natural languages frequently used all over the world can be recognized with a higher accuracy (including languages written from right to left and from left to right). The association relations between the input strokes and the character strings can be provided. It could support any strokes with irregular written orders.

Handwriting verification methods and related computer systems, and handwriting-based user authentication methods and related computer systems are disclosed. A handwriting verification method comprises obtaining a handwriting test sample containing a plurality of available parameters, extracting geometric parameters, deriving geometric features comprising an x-position value and a y-position value for each of a plurality of feature points in the test sample, performing feature matching between geometric features of the test sample and a reference sample, determining a handwriting verification result based at least in part on the feature matching, and outputting the handwriting verification result. Techniques and tools for generating and preserving electronic handwriting data also are disclosed. Raw handwriting data is converted to a streamed format that preserves the original content of the raw handwriting data. Techniques and tools for inserting electronic handwriting data into a digital image also are disclosed.

An input display device includes a display unit that displays a screen for handwriting input on an input screen including a plurality of input fields, a stroke data processing unit that groups stroke data which is input to a capture screen by handwriting into stroke data representing characters to generate grouped stroke data as grouping stroke data, a character recognition unit that conducts character recognition on the grouping stroke data to convert the grouping stroke data into at least one recognized character, and a control processing unit that displays the at least one recognized character at the plurality of input fields of the input screen correlated with positions, at which handwriting input was performed, in the screen for handwriting input.

In an optical character recognition (OCR) method for digitizing printed text images using a long-short term memory (LSTM) network, text images are pre-processed using a stroke-aware max-min pooling method before being fed into the network, for both network training and OCR prediction. During training, an average stroke thickness is computed from the training dataset. Stroke-aware max-min pooling is applied to each text line image, where minimum pooling is applied if the stroke thickness of the line is greater than the average stroke thickness, while max pooling is applied if the stroke thickness is less than or equal to the average stroke thickness. The pooled images are used for network training. During prediction, stroke-aware max-min pooling is applied to each input text line image, and the pooled image is fed to the trained LSTM network to perform character recognition.

In an optical character recognition (OCR) method for digitizing printed text images using a long-short term memory (LSTM) network, text images are pre-processed using a stroke-aware max-min pooling method before being fed into the network, for both network training and OCR prediction. During training, an average stroke thickness is computed from the training dataset. Stroke-aware max-min pooling is applied to each text line image, where minimum pooling is applied if the stroke thickness of the line is greater than the average stroke thickness, while max pooling is applied if the stroke thickness is less than or equal to the average stroke thickness. The pooled images are used for network training. During prediction, stroke-aware max-min pooling is applied to each input text line image, and the pooled image is fed to the trained LSTM network to perform character recognition.

A method for facilitating handwriting practice includes: generating handwriting strokes in response to user input of user-writing strokes; generating an input image that includes the handwriting strokes, and that has a shape similar to a shape of a standard image associated with a standard word character; scaling the input image to generate a scaled image with a size that is the same as a size the standard image; overlapping the standard image and the scaled image; comparing an n.sup.th handwriting stroke in the scaled image with an n.sup.th standard stroke in a standard order of the standard word character; and when the n.sup.th handwriting stroke does not correspond in position to the n.sup.th standard stroke, displaying a notification of a stroke order error.

The present disclosure relates to a method and system of processing original handwriting input, the system and method being capable of recognize a plurality of strokes provided on the input recognition interface, the method including: determining a stroke box around each stroke; determining overlap between the stroke boxes; correlating overlapping stroke boxes to one or more characters; providing a character box around each of the one or more characters; determining overlap between character boxes; correlating overlapping character boxes to one or more words; providing a word box around each of the one or more words; provide a word margin around each of the one or more word boxes; determining overlap between each word box to determine a line; wherein each of the characters, words, or lines can be individually selected and rearranged, the system automatically adjusting spacing or placement of surrounding elements to allow for the rearrangement.

Analog written content of handwritten drawings or written characters can be transformed into digital ink via an analog-to-ink service. The analog-to-ink service can receive a static image of the analog written content, extract analog strokes from other information, such as background, in the static image, and then convert the analog strokes to digital ink strokes, for example, by populating an ink container with at least two parameters for defining the digital ink strokes. The at least two parameters can include a pressure, at tilt, a direction, a beginning point, an end point, a direction, a color, an order, an overlap, a language, and a time. The analog-to-ink service can provide the ink container to a content creation application that supports inking so that a user can manipulate the content in an inking environment.