Method and system for optical character recognition of series of images

1. A method, comprising:

receiving, by a processing device, a current image of a series of images of an original document, wherein the current image at least partially overlaps with a previous image of the series of images;

performing optical character recognition (OCR) of the current image to produce an OCR text and a corresponding text layout;

associating at least part of the OCR text with a first cluster of a plurality of clusters of symbol sequences associated with one or more previously received images of the series of images;

identifying a first string representing the first cluster of symbol sequences based on a first subset of images of the series of images;

identifying a first template field of a document template corresponding to the first cluster based on the first string representing the first cluster and the text layout of the current image;

identifying, for the first cluster, a second-level string representing the first cluster of symbol sequences based on one or more parameters of the first template field; and

producing, using the second-level string, a resulting OCR text representing at least a portion of the first template field of the original document.

2. The method of claim 1 further comprising:

normalizing one or more symbol sequences from the first cluster to conform the one or more symbol sequences to the first parameters of the first template field; and

identifying, for the first cluster, the second-level string representing the first cluster of symbol sequences based on the normalized symbol sequences.

3. The method of claim 2 wherein normalizing comprises:

identifying one or more unconforming symbols in the symbol sequences, wherein the unconforming symbol is a symbol that does not satisfy the first parameters of the first template field; and

replacing the unconforming symbols in the symbol sequences with blank spaces.

4. The method of claim 1 further comprising

analyzing the symbol sequences from the first cluster to identify symbol sequences that satisfy first parameters of the first template field; and

filtering the symbol sequences from the first cluster.

5. The method of claim 4 , wherein the second-level string is a constrained string, satisfying second parameters of the first template field.

6. The method of claim 1 , further comprising:

determining that the second-level string is not to be identified; and

identifying, for the first cluster, a third string representing the first cluster of symbol sequences based on a second subset of images of the series of images different from the first subset of images.

7. The method of claim 6 wherein the second subset of the series of images comprises the first subset of the series of images.

8. The method of claim 1 further comprising:

identifying the document template corresponding to the original document based at least on the text layout, produced by the OCR, and the identified first string.

9. The method of claim 8 wherein the document template is selected from a set of document templates.

10. The method of claim 8 further comprising:

determining that the document template for the original document is not to be identified with a particular confidence level; and

identifying a fourth string representing the first cluster of symbol sequences based on a third subset of images of the series of images different from the first subset of images.

11. A system, comprising:

a memory;

a processing device, coupled to the memory, the processing device configured to:

receive a current image of a series of images of an original document, wherein the current image at least partially overlaps with a previous image of the series of images;

perform optical character recognition (OCR) of the current image to produce an OCR text and a corresponding text layout;

associate at least part of the OCR text with a first cluster of a plurality of clusters of symbol sequences associated with one or more previously received images of the series of images;

identify a first string representing the first cluster of symbol sequences based on a first subset of images of the series of images;

identify a first template field of a document template corresponding to the first cluster based on the first string representing the first cluster and the text layout of the current image;

identify, for the first cluster, a second-level string based on one or more parameters of the first template field; and

produce, using the second-level string, a resulting OCR text representing at least a portion of the first template field of the original document.

12. The system of claim 11 wherein the processing device is further to:

normalize one or more symbol sequences from the first cluster to conform the symbol sequences to the first parameters of the first template field; and

identify, for the first cluster, the second-level string representing the first cluster of symbol sequences based on a plurality of the normalized symbol sequences.

13. The system of claim 12 wherein to normalize one or more symbol sequences, the processing device is to:

identify one or more unconforming symbols in the symbol sequences, wherein the unconforming symbol is a symbol that does not satisfy first parameters of the first template field; and

replace the unconforming symbols in the symbol sequences with blank spaces.

14. The system of claim 11 wherein the processing device is further to analyze the symbol sequences from the first cluster to identify symbol sequences satisfying first parameters of the first template field to filter the symbol sequences from the first cluster.

15. The system of claim 14 , wherein the second-level string is a constrained string, satisfying second parameters of the first template field.

16. The system of claim 11 , wherein the processing device is further to:

determine that the second-level string is not to be identified; and

identify, for the first cluster, a third string representing the first cluster of symbol sequences based on a second subset of images of the series of images different from the first subset of images.

17. The system of claim 11 wherein the processing device is further to:

identify the document template corresponding to the original document based at least on the text layout, produced by the OCR, and the identified first string.

18. The system of claim 17 wherein the processing device is further to:

determine that the document template for the original document is not to be identified with a particular confidence level; and

identify a fourth string representing the first cluster of symbol sequences based on a third subset of images of the series of images different from the first subset of images.

19. A computer-readable non-transitory storage medium comprising executable instructions that, when executed by a processing device, cause the processing device to:

receive a current image of a series of images of an original document, wherein the current image at least partially overlaps with a previous image of the series of images;

perform optical character recognition (OCR) of the current image to produce an OCR text and a corresponding text layout;

associate at least part of the OCR text with a first cluster of a plurality of clusters of symbol sequences associated with one or more previously received images of the series of images;

identify a first string representing the first cluster of symbol sequences based on a first subset of images of the series of images;

identify a first template field of a document template corresponding to the first cluster based on the first string representing the first cluster and the text layout of the current image;

identify, for the first cluster, a second-level string representing the first cluster of symbol sequences based on one or more parameters of the first template field; and

produce, using the second-level string, a resulting OCR text representing at least a portion of the first template field of the original document.

20. The system of claim 19 wherein the processing device is further to:

normalize one or more symbol sequences from the first cluster to conform the symbol sequences to the first parameters of the first template field; and

identify, for the first cluster, the second-level string representing the first cluster of symbol sequences based on a plurality of the normalized symbol sequences.