Method and apparatus for character recognition of handwritten input

An apparatus for recognizing a handwritten symbol comprising;

• (A) a digitizing tablet for receiving handwritten input, said handwritten input having a sequence of (x, y, pen) points where x and y coordinates are in a two dimensional coordinate system and pen is a binary value indicating an associated pen-up/pen-down state;
• (B) a memory for storing an input sequence of strokes, said sequence of strokes representing the sequence of points, each stroke representing a basic unit used to construct at least one handwritten symbol of a character set;
• (C) a processor for generating a shortlist of candidate symbols the character set that most likely represent the input by finding a fast matching distance between the input sequence of strokes and a sequence of strokes representing each symbol in a vocabulary, said sequence of strokes representing each symbol in the vocabulary being derived from statistical analysis of a plurality of samples of handwritten symbols;
• and
• (D) a processor for generating a final sorted list of candidate symbols of the vocabulary which most likely represents the handwritten input by finding a detailed matching distance between the input sequence of strokes and the sequence of strokes for each symbol in the said shortlist of candidate symbols in the vocabulary that most likely represents the handwritten input.

A method of recognizing handwritten input comprising the steps of:

• receiving handwritten input;
• preprocessing the handwritten input to represent a string of strokes;
• analyzing the handwritten input by matching of the string of strokes and a sequence of strokes for a plurality of symbols in a vocabulary to provide a shortlist of candidate characters which most likely correspond to the handwritten input;
• accessing a plurality of reference templates corresponding to only the candidate characters of the shortlist and comparing the handwritten input with at least one of said reference templates to identify at least one candidate character most likely to represent said handwritten input.

A method of recognizing a handwritten symbol comprising the steps of:

• receiving a handwritten input as a sequence of (x, y, pen)points where x and y are coordinates in a two dimensional coordinate system and pen is a binary value indicating an associated pen-down state;
• converting the sequence of points into a sequence of strokes, each stroke representing a basic unit used to construct any handwritten symbol in a vocabulary;
• parametizing each stroke using a four dimensional vector having an x coordinate of the midpoint of the stroke, a y coordinate of the midpoint of the stroke, a length representing the length of the stroke, and an angle representing the angle of the stroke with respect to a reference axis.
• determining a shortlist of candidate symbols in the vocabulary that are likely matches for the input by finding a fast matching distance between the input sequence of strokes and the sequence of strokes for each symbol in the vocabulary, said sequence of strokes for each symbol in the vocabulary being derived from statistical analysis on samples of the handwritten symbols;
• and
• determining a final sorted list of candidate symbols in the vocabulary that are likely matches for the input by finding a detailed matching distance between the input sequence of strokes and the sequence of strokes for each symbol in the shortlist of candidate symbols in the vocabulary that are likely matches for the handwritten input.

A method of recognizing a handwritten symbol comprising the steps of:

• providing a vocabulary of handwritten symbols, each symbol represented as a sequence of strokes, each stroke representing a basic unit used to construct any handwritten symbol in the vocabulary, said sequence of strokes for each symbol in the vocabulary being derived from statistical analysis on samples of the handwritten symbols;
• receiving a handwritten input as a sequence of (x, y, pen) points where x and y are coordinates in a two dimensional coordinate system and pen is a binary value indicating an associated pen-down state;
• converting the sequence of points into a sequence of strokes;
• parametizing each stroke using a vector having an x coordinate of the midpoint of the stroke, a y coordinate of the midpoint of the stroke, a length representing the length of the stroke, and an angle representing the angle of the stroke with respect to a reference axis;
• associating different weighting values to different strokes, said weighting values being derived from statistical analysis on a plurality of handwritten symbols;
• determining a list of candidate symbols in the vocabulary that are likely matches for the input by finding a matching distance between the input sequence of strokes and the sequence of strokes for each symbol in the vocabulary, taking account of the weighting values.

A method, comprising the steps of:

• receiving handwritten input as data representing a sequence of strokes;
• determining a shortlist of a plurality of candidate symbols from stored templates that are likely matches for the handwritten input by comparing one or more stroke parameters between the sequence of strokes representing the handwritten input and the sequence of strokes for a plurality of symbols from the templates;
• and
• determining one or more recognized symbols that are likely matches for the handwritten input by comparing two or more stroke parameters between the sequence of strokes representing the handwritten input and the sequence of strokes for each symbol in the shortlist that are likely matches for the handwritten input.

A method, comprising the steps of:

• processing handwritten input as a sequence of handwritten strokes to provide data representing a sequence of straight strokes;
• determining a shortlist of a plurality of candidate symbols from stored templates that are likely matches for the handwritten input by comparing one or more stroke parameters between the sequence of straight strokes representing the handwritten input and the sequence of strokes for a plurality of symbols from the templates;
• and
• determining one or more recognized symbols that are likely matches for the handwritten input by comparing two or more stroke parameters between the sequence of straight strokes representing the handwritten input and the sequence of strokes for each symbol in the shortlist that are likely matches for the handwritten input.

A method, comprising the steps of:

• receiving handwritten input as data representing a sequence of strokes;
• determining an angle parameter for each of the sequence of strokes, the angle parameter representing an angle of the stroke from a reference axis;
• determining a shortlist of a plurality of candidate symbols from stored templates that are likely matches for the handwritten input by comparing the angle parameter between the sequence of strokes representing the handwritten input and the sequence of strokes for a plurality of symbols from the templates;
• and
• determining one or more recognized symbols that are likely matches for the handwritten input by comparing other stroke parameters between the sequence of strokes representing the handwritten input and the sequence of strokes for each symbol in the shortlist that are likely matches for the handwritten input.

A method, comprising the steps of:

• receiving handwritten input as data representing a sequence of strokes;
• determining stroke parameters for each of the sequence of strokes, the stroke parameters selected from the group of parameters consisting of:
• an angle parameter representing an angle of the stroke from a reference axis;
• a x coordinate midpoint parameter representing the x coordinate of the midpoint of the stroke;
• a y coordinate midpoint parameter representing the y coordinate of the midpoint of the stroke;
• a length parameter representing stroke length;
• determining a plurality of candidate symbols from stored templates that are likely matches for the handwritten input by comparing one stroke parameter between the sequence of strokes representing the handwritten input and the sequence of strokes for a plurality of symbols from the templates;
• and
• determining one or more recognized symbols that are likely matches for the handwritten input by comparing two or more stroke parameters between the sequence of strokes representing the handwritten input and the sequence of strokes for each symbol in the plurality of candidate symbols that are likely matches for the handwritten input.

A method of recognizing a handwritten symbol comprising the steps of:

• (A) receiving a handwritten input as a sequence of (x, y) points where x and y are coordinates in a two dimensional coordinate system;
• (B) converting the sequence of points into a sequence of strokes, each stroke representing a basic unit used to construct any handwritten symbol in a vocabulary;
• (C) determining a shortlist of candidate symbols in the vocabulary that are likely matches for the input by finding a fast matching distance between the input sequence of strokes and the sequence of strokes for each symbol in the vocabulary, said sequence of strokes for each symbol in the vocabulary being derived from statistical analysis on samples of the handwritten symbols, said step of determining a shortlist including comparing a difference between a number of strokes in an input sequence of strokes and a number of strokes in a template with a threshold;
• and
• (D) determining a final sorted list of candidate symbols in the vocabulary that are likely matches for the input by finding a minimum dynamic programming matching distance between the input sequence of strokes and the sequence of strokes for each symbol in the shortlist of candidate symbols in the vocabulary that are likely matches for the handwritten input.

A method of recognizing a handwritten symbol comprising the steps of:

• (A) receiving a handwritten input as a sequence of (x, y) points where x and y are coordinates in a two dimensional coordinate system;
• (B) converting the sequence of points into a sequence of strokes, each stroke representing a basic unit used to construct any handwritten symbol in a vocabulary;
• (C) determining a shortlist of candidate symbols in the vocabulary that are likely matches for the input by finding a fast matching distance between first and second sets of strokes, where one of the first and second sets of strokes is the input sequence of strokes and the other is the sequence of strokes for each symbol in the vocabulary, said sequence of strokes for each symbol in the vocabulary being derived from statistical analysis on samples of the handwritten symbols, said step of finding a fast matching distance including calculating distances between a stroke in one of the sets of strokes and neighboring strokes in the other of the sets of strokes;
• and
• (D) determining a final sorted list of candidate symbols in the vocabulary that are likely matches for the input by finding a minimum dynamic programming matching distance between the input sequence of strokes and the sequence of strokes for each symbol in the shortlist of candidate symbols in the vocabulary that are likely matches for the handwritten input.