Claims:We claim:
1. A method of correcting a fabrication in a document, the method comprising:
receiving, by a fabrication correction system (101), an input document from a user;
identifying, by the fabrication correction system (101), a reference document based on a category of the input document, from a reference document database;
detecting, by the fabrication correction system (101), fabrication in the input document based on a predefined machine learning technique, by comparing one or more parameters associated with the input document with corresponding one or more parameters of the reference document;
determining, by the fabrication correction system (101), one or more fabricated regions in the input document and a type of the fabrication in each of the one or more fabricated regions, based on one or more predefined techniques; and
performing, by the fabrication correction system (101), one or more actions in each of the one or more fabricated regions based on the type of the fabrication and the reference document, upon receiving a user input, for correcting the one or more fabricated regions.

2. The method as claimed in claim 1, wherein the input document comprises at least one of text and one or more images.

3. The method as claimed in claim 1, wherein the category of the input document is identified based on the one or more parameters associated with the input document.

4. The method as claimed in claim 1, wherein the category comprises official document, non-official document, and social media related document.

5. The method as claimed in claim 3, wherein the one or more parameters comprises text, font of the text, pixel, height, and width associated with at least one of text and one or more images in the input document.

6. The method as claimed in claim 1, wherein the type of the fabrication comprises variation in alignment, font, wavelength, syntax pixels, ink characteristics and opacity and nature of distortion associated with at least one of text and one or more images of the input document.

7. The method as claimed in claim 1 further comprising:
providing a notification to the user upon detecting the fabrication; and
receiving the user input for correction of the one or more fabricated regions.

8. The method as claimed in claim 1, wherein performing the one or more actions in the one or more fabricated regions comprises generating a new input document, based on the predefined machine learning technique, by correcting each of the one or more fabricated regions using the type of the fabrication and the reference document.

9. The method as claimed in claim 1, wherein the predefined machine learning technique is Generative Adversarial Network (GAN).

10. A fabrication correction system (101) for correcting a fabrication in a document, comprising:
a processor (113); and
a memory (111) communicatively coupled to the processor (113), wherein the memory (111) stores processor instructions, which, on execution, causes the processor (113) to:
receive an input document from a user;
identify a reference document based on a category of the input document, from a reference document database;
detect fabrication in the input document based on a predefined machine learning technique, by comparing one or more parameters associated with the input document with corresponding one or more parameters of the reference document;
determine one or more fabricated regions in the input document and a type of the fabrication in each of the one or more fabricated regions, based on one or more predefined techniques; and
perform one or more actions in each of the one or more fabricated regions based on the type of the fabrication and the reference document, upon receiving a user input, for correcting the one or more fabricated regions.

11. The fabrication correction system (101) as claimed in claim 10, wherein the input document comprises at least one of text and one or more images.

12. The fabrication correction system (101) as claimed in claim 10, wherein the processor identifies category of the input document based on the one or more parameters associated with the input document.

13. The fabrication correction system (101) as claimed in claim 10, wherein the category comprises official document, non-official document, and social media related document.
14. The fabrication correction system (101) as claimed in claim 12, wherein the one or more parameters comprises text, font of the text, pixel, height, and width associated with at least one of text and one or more images in the input document.

15. The fabrication correction system (101) as claimed in claim 10, wherein the type of the fabrication comprises variation in alignment, font, wavelength, syntax pixels, ink characteristics and opacity and nature of distortion associated with at least one of text and one or more images of the input document.

16. The fabrication correction system (101) as claimed in claim 10, wherein the processor:
provides a notification to the user upon detecting the fabrication; and
receives the user input for correction of the one or more fabricated regions.

17. The fabrication correction system (101) as claimed in claim 10, wherein the processor performs the one or more actions in the one or more fabricated regions by generating a new input document, based on the predefined machine learning technique, by correcting each of the one or more fabricated regions using the type of the fabrication and the reference document.

18. The fabrication correction system (101) as claimed in claim 10, wherein the predefined machine learning technique is Generative Adversarial Network (GAN).

Dated this 12th day of June, 2018

R Ramya Rao
Of K&S Partners
Agent for the Applicant
IN/PA-1607
, Description:TECHNICAL FIELD
The present subject matter is related in general to document authentication, more particularly, but not exclusively to a method and system of correcting fabrication in a document.