LICENSE PLATE RECOGNITION WITH LOW-RANK, SHARED CHARACTER CLASSIFIERS

• US 20180101750A1
• Filed: 10/11/2016
• Published: 04/12/2018
• Est. Priority Date: 10/11/2016
• Status: Abandoned Application

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Soldevila, Albert Gordo
• Application Number:
  US15/290,561
• Publication Number:
  US 20180101750A1
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/583 using metadata automatically derived from the content
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06V 10/809 of classification results, e.g. where the classifiers operate on the same input data
• CPC Class Codes: G06V 10/82 using neural networks
• CPC Class Codes: G06V 20/625 License plates
• CPC Class Codes: G06V 30/10 Character recognition
• CPC Class Codes: G06V 30/18057 Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]

Abstract

A method is disclosed for performing multiple classification of an image simultaneously using multiple classifiers, where information between the classifiers is shared explicitly and is achieved with a low-rank decomposition of the classifier weights. The method includes applying an input image to classifiers and, more particularly, multiplying the extracted input image features by |Σ| embedding matrices Ŵc to generate a latent representation of d-dimensions for each of the |Σ| characters. The embedding matrices are uncorrelated with a position of the extracted character. The step of applying the extracted character to the classifiers further includes projecting the latent representation with a decoding matrix shared by all the character embedding matrices to generate scores of every character in an alphabet at every position. At least one of the multiplying the extracted input image features and the projecting the latent representation with the decoding matrix are performed with a processor.

Claims

1. A method to perform multiple classification of an image simultaneously using multiple classifiers, where information between the classifiers is shared explicitly and is achieved with a low-rank decomposition of the classifier weights, the method comprising:

acquiring an input image;

extracting a representation from the input image;

applying the low-rank character classifiers to the extracted image representation, including;

multiplying the extracted image representation by |Σ

| embedding matrices Ŵ

c to generate a latent representation of d-dimensions for each of the |Σ

| characters, wherein the embedding matrices are uncorrelated with a position of the extracted character;

projecting the latent representation with a decoding matrix shared by all the character embedding matrices to generate scores of every character in an alphabet at every position;

wherein at least one of the multiplying the extracted representation from the input and the projecting the latent representation with the decoding matrix are performed with a processor.

2. The method of claim 1, wherein the decoding matrix is indicative of a probability that a given character is found at each position.

3. The method of claim 1 further comprising:

outputting a prediction corresponding to a probability that a particular character appears in each possible position of the input image.

4. The method of claim 3, wherein the outputting the prediction includes:

assigning a character label with a highest score at each position to the input image.

5. The method of claim 1, wherein the multiplying the input by |Σ

| embedding matrices Ŵ

c includes;

projecting the input image into a different space of |Σ

|×

d-dimensions representing every character in the alphabet in a space of d-dimensions.

6. The method of claim 1, wherein the input image is a word image.

7. The method of claim 6, wherein the word image is a license plate.

8. The method of claim 7 further comprising:

determining a license plate configuration using character labels assigned at each possible position of the input image, wherein the each possible position in the word image is associated with one of a letter, a number, and a null character.

9. The method of claim 1 further comprising:

forcing the classifiers to share information by decomposing the classifiers into the |Σ

| embedding matrices Ŵ

c and the decoding matrix P.

10. The method of claim 1 further comprising training the classifiers, including:

randomly initializing an embedding matrix for each possible position in a sample word image;

for the sample word image, computing character scores for a given character-position in the sample word image by projecting the extracted image representation into the latent space with Ŵ

c and then decoding the results with P;

independently applying a soft max to each row to make the output of different positional characters at a same position comparable;

computing cross-entropy losses;

back-propagating the computed losses through a neural network to generate gradients; and

updating weights of the embedding matrices using the gradients.

11. A system for performing multiple classification of an image simultaneously using multiple classifiers, where information between the classifiers is shared explicitly and is achieved with a low-rank decomposition of the classifier weights, the system comprising:

a processor; and

a non-transitory computer readable memory storing instructions that are executable by the processor to;

acquire an input image;

extract a character from the input image;

applying the extracted character to at least one classifier;

a classifier, including;

|Σ

| embedding matrices Ŵ

c each uncorrelated with a position of the extracted character, wherein the processor multiplies the extracted input image representation by the |Σ

| embedding matrices Ŵ

c to generate a latent representation of d-dimensions for each of the |Σ

| characters; and

,

a decoding matrix shared by all the character embedding matrices, wherein the processor projects the latent representation with the decoding matrix to generate scores of every character in an alphabet at every position.

12. The system of claim 11, wherein the decoding matrix is indicative of a probability that a given character is found at each position.

13. The system of claim 11, wherein the processor is further operative to:

output a prediction corresponding to a probability that a particular character appears in each possible position of the input image.

14. The system of claim 13, wherein the processor is operative to output the prediction by assigning a character label with a highest score at each position to the input image.

15. The system of claim 11, wherein the processor is operative to multiply the input by |Σ

| embedding matrices Ŵ

c by projecting the input image into a different space of |Σ

|×

d-dimensions representing every character in the alphabet in a space of d-dimensions.

16. The system of claim 11, wherein the input image is a word image.

17. The system of claim 16, wherein the word image is a license plate.

18. The system of claim 17, wherein the processor is further operative to:

determine a license plate configuration using character labels assigned at each possible position of the input image, wherein the each possible position in the word image is associated with one of a letter, a number, and a null character.

19. The system of claim 11, wherein the processor is further operative to:

force the classifiers to share information by decomposing the classifiers into the |Σ

| embedding matrices Ŵ

c and the decoding matrix P.

20. The system of claim 11 wherein the processor is further to train the classifiers, including:

randomly initializing an embedding matrix for each possible position in a sample word image;

for the sample word image, computing character scores for a given character-position in the sample word image by projecting the extracted image representation into the latent space with Ŵ

c and then decoding the results with P;

independently applying a soft max to each row to make the output of different positional characters at a same position comparable;

computing cross-entropy losses;

back-propagating the computed losses through a neural network to generate gradients; and

updating weights of the embedding matrices using the gradients.