US 2004/0213455 A1 · Training an on-line handwriting recognizer

Abstract

Character model graphs are created, and the parameters of the model graphs are adjusted to optimize character recognition performed with the model graphs. In effect the character recognizer using the model graphs is trained. The model graphs are created in three stages. First, a vector quantization process is used on a set of raw samples of handwriting symbols to create a smaller set of generalized reference characters or symbols. Second, a character reference model graph structure is created by merging each generalized form model graph of the same character into a single character reference model graph. The merging is based on weighted Euclidian distance between parts of trajectory assigned to graph edges. As a last part of this second stage “type-similarity” vectors are assigned to model edges to describe similarities of given model edge to each shape and to each possible quantized value of other input graph edge parameters. Thus, similarity functions, or similarity values, are defined by different tables on different model edges. In the third stage, model creation further consists of minimizing recognition error by adjusting model graphs parameters. An appropriate smoothing approximation is used in the calculation of similarity score between input graph and model graphs. The input graph represents a word from a work sample set used for training, i.e. adjusting the model graph parameters. A recognition error is calculated as a function of the difference between similarity scores for best answers and the one correct answer for the word being recognized. The gradient of the recognition error as a function of change in parameters is computed and used to adjust the parameters. Model graphs with adjusted parameters are then used to recognize the words in a test set, and a percent of correct recognitions in the test set is calculated. The recognition error calculation with the work set, the parameter adjustment process, and the calculation of the percent of correct recognitions with the test set is repeated. After a number of iterations of this process, the optimum set of parameters for the model graphs will be found.

Claims29

Dependent claims

A method for creating character model graphs trained for on-line recognizer of handwritten text to optimize the correct recognition percentage of the recognizer, the method comprising:

creating character model graphs from typical shapes of characters, the character model graphs having a set of adjustable parameters;
recognizing sample words in a work set using the character model graphs;
evaluating the word recognition error for each sample word;
calculating the gradient of the word recognition error as function of the adjustable parameters;
calculating a work set recognition error gradient;
adjusting the adjustable parameters of the character model graphs to update the set of adjustable parameters to provide an updated set;
recognizing test words in a test set using character model graphs with the updated set of adjustable parameters;
calculating a correct recognition percent for the test set when using the model graphs with the updated set of adjustable parameters;
and
iterating the above sequence of acts until the correct recognition value reaches an optimum value;
and
selecting the updated set of adjustable parameters yielding the correct recognition value having the optimum value as the set of adjustable parameters to be used in character model graphs.

9 more elements

A computer readable medium for storing computer instructions for a computer process for training character model graphs to optimize the recognition of text by a recognizer using the character model graphs, the computer process comprising:

recognizing words from a first set of words using a current parameter set for the character model graphs and generating a recognition error gradient for the first set based on changes in the word recognition error as a function of changes in parameters in the current parameter set of the character model graphs;
adjusting the parameters of the character model graphs in response to the recognition error gradient to create an updated parameter set for the character model graphs;
recognizing words from a second set of words using the updated parameter set and generating a correct recognition value, the correct recognition value being associated with the updated parameter set for the character model graphs;
and
repeating all of the above acts until the correct recognition value reaches a transition value and selecting the parameter set associated with the transition value as the optimum parameters for the character model graphs.

2 more elements

Dependencies are inferred from the claim text.

Training an on-line handwriting recognizer

Life of this patent

Training an on-line handwriting recognizer

Life of this patent

Abstract

Claims29