Methods and systems of handwriting recognition in virtualized-mail services

1. A computerized method useful for handwriting recognition (HWR) on physical mail envelopes addressed to a user comprising:

scanning a physical mail item to obtain a digital image of the address-side of the physical mail item;

identifying that at least one of a return address region or a recipient address region of the address-side of the physical mail item;

determining that the at least one of the return address region or a recipient address region comprises a handwritten text;

providing a data store of known senders to the recipient address, wherein the data store of known senders comprises a data store of return address information in a known sender handwriting samples and a data store of receiver address information in the known sender handwriting samples;

providing a data store of a receiver's identity and address;

creating a first training set comprising the historical data store of known senders;

training the neural network in a first stage using the first training set;

creating a second training set for a second stage of training comprising the data store of a receiver's identity and address;

training the neural network in a second stage using the second training set; and

integrating the neural network into an HWR functionality.

2. The computerized method of claim 1 further comprising:

using the HWR functionality to convert a content of the return address region and the recipient address region to a computer-readable text.

3. The computerized method of claim 2 further comprising:

determining a recipient identity and a recipient address from the computer-readable text.

4. The computerized method of claim 3 further comprising:

determining a sender identity and a sender address from the computer-readable text.

5. The computerized method of claim 4 , wherein the physical mail item comprises an envelope.

6. The computerized method of claim 5 , wherein the return address region comprises a region in the upper left corner of the of the address-side of the physical mail item.

7. The computerized method of claim 6 , wherein recipient address region comprises a name of the user, a name of the user's company, an address of the user or an address of the user's company written in parallel to a longest side of the envelope.

8. The computerized method of claim 7 further comprising:

providing a data store of a frequency of the sender identity to the receiver identity.

9. The computerized method of claim 8 further comprising:

integrating the frequency of the sender identity to the receiver identity into the first training set or the second training set.

10. A computerized system useful for handwriting recognition (HWR) on physical mail envelopes addressed to a user comprising:

at least one processor configured to execute instructions;

at least one memory containing instructions when executed on the at least one processor, causes the at least one processor to perform operations that:

scan a physical mail item to obtain a digital image of the address-side of the physical mail item;

identify that at least one of a return address region or a recipient address region of the address-side of the physical mail item;

determine that the at least one of the return address region or a recipient address region comprises a handwritten text;

provide a data store of known senders to the recipient address, wherein the data store of known senders comprises a data store of return address information in a known sender handwriting samples and a data store of receiver address information in the known sender handwriting samples;

provide a data store of a receiver's identity and address;

create a first training set comprising the historical data store of known senders;

train the neural network in a first stage using the first training set;

create a second training set for a second stage of training comprising the data store of a receiver's identity and address;

train the neural network in a second stage using the second training set; and

integrate the neural network into an HWR functionality.

11. The computerized system of claim 10 , wherein the least one memory containing instructions when executed on the at least one processor, causes the at least one processor to perform operations that:

use the HWR functionality to convert a content of the return address region and the recipient address region to a computer-readable text.

12. The computerized system of claim 11 , wherein the least one memory containing instructions when executed on the at least one processor, causes the at least one processor to perform operations that:

determine a recipient identity and a recipient address from the computer-readable text.

13. The computerized system of claim 12 , wherein the least one memory containing instructions when executed on the at least one processor, causes the at least one processor to perform operations that:

determine a sender identity and a sender address from the computer-readable text.

14. The computerized system of claim 4 , wherein the physical mail item comprises an envelope.

15. The computerized system of claim 14 , wherein the return address region comprises a region in the upper left corner of the of the address-side of the physical mail item.

16. The computerized system of claim 15 , wherein recipient address region comprises a name of the user, a name of the user's company, an address of the user or an address of the user's company written in parallel to a longest side of the envelope.

17. The computerized system of claim 16 , wherein the least one memory containing instructions when executed on the at least one processor, causes the at least one processor to perform operations that:

provide a data store of a frequency of the sender identity to the receiver identity.

18. The computerized system of claim 8 , wherein the least one memory containing instructions when executed on the at least one processor, causes the at least one processor to perform operations that:

integrate the frequency of the sender identity to the receiver identity into the first training set or the second training set.