Innovative OCR systems and methods that combine a template based generative model with a discriminative model
AssigneePalo Alto Research Center Incorporated
InventorsJindong Chen, Yizhou Wang
Life of this patent
Estimating the term…
Abstract
A method employing a hybrid classification model is used to perform optical character recognition operations for an image. Image data from the image is provided to a generative classification model of the hybrid model, and generative image classifications operations are performed, generating a feature data set which is outputted from the generative classification model. This feature data set is then provided to the discriminative classification model, and discriminative classification operations are performed to generate a classification of the image.
Claims15
A method of performing optical character recognition operations on an image:
- providing image data of the image to a generative classification model;
- using a computer processor, performing generative image classification operations on the image data by the generative classification model;
- outputting a feature data set of the image data;
- providing the feature data set to a discriminative classification model;
- performing discriminative classification operations on the provided feature data set;
- and
- generating a classification of the image;
- wherein the feature data set is a feature vector defined as:
- ϕ
- c
- ,
- i
- ,
- j
-
- (
- I
- g
- )
- =
- -
- I
- g
-
- (
- i
- ,
- j
- )
- -
- ϑ
- c
-
- (
- i
- ,
- j
- )
- ϑ
- c
-
- (
- i
- ,
- j
- )
-
- (
- 1
- -
- ϑ
- c
-
- (
- i
- ,
- j
- )
- )
- (
- 3
- )
- where Ig (i,j) is the pixel value at location (i,j) of the observed character image, and θC(i,j) is an estimated value derived from the character template of character c at Location (i,j).
A hybrid classification model for optical character recognition, the hybrid classification model comprising:
- a generative classification model stored in computer memory;
- and
- a discriminative classification model, combined with the generative classification model and configured to use a function data set from the generative classification model;
- wherein the feature data set is a feature vector defined as:
- ϕ
- c
- ,
- i
- ,
- j
-
- (
- I
- g
- )
- =
- -
- I
- g
-
- (
- i
- ,
- j
- )
- -
- ϑ
- c
-
- (
- i
- ,
- j
- )
- ϑ
- c
-
- (
- i
- ,
- j
- )
-
- (
- 1
- -
- ϑ
- c
-
- (
- i
- ,
- j
- )
- )
- ,
- where Ig (i,j) is the pixel value at location (i,j) of the observed character image, and θ(l) is an estimated value derived from the character templates.
A computer program product, operative in a data processing system and implemented on a computer readable medium for classifying an image comprising:
- providing image data of the image to a generative classification model;
- performing generative image classification operations on the image data by the generative classification model;
- outputting a feature data set of the image data;
- providing the feature data set to a discriminative classification model;
- performing discriminative classification operations on the provided feature data set;
- and
- generating a classification of the image;
- wherein the feature data set is a feature vector defined as:
- ϕ
- c
- ,
- i
- ,
- j
-
- (
- I
- g
- )
- =
- -
- I
- g
-
- (
- i
- ,
- j
- )
- -
- ϑ
- c
-
- (
- i
- ,
- j
- )
- ϑ
- c
-
- (
- i
- ,
- j
- )
-
- (
- 1
- -
- ϑ
- c
-
- (
- i
- ,
- j
- )
- )
- (
- 3
- )
- where Ig (i,j) is the pixel value at location (i,j) of the observed character image, and θc (i,j) is an estimated value derived from the character template of character c at Location (i, j).
Dependencies are inferred from the claim text.