Zero-footprint image capture by mobile device
A computer-implemented method for image capture by a mobile device, comprising: receiving, by a video capturing application running on a mobile device, a video stream from a camera of the mobile device; identifying a specific frame of the video stream; generating a plurality of hypotheses defining image borders within the specific frame; selecting, by a neural network, a particular hypothesis among the plurality of hypotheses; producing a candidate image by applying the particular hypothesis to the specific frame; determining a value of a quality metric of the candidate image; determining that the value of the quality metric of the candidate image exceeds one or more values of the quality metric of one or more previously processed images extracted from the video stream; wherein the image capture application is a zero-footprint application.
1. A computer-implemented method for image capture by a mobile device, comprising:
receiving, by a video capturing application running on a mobile device, a video stream from a camera of the mobile device;
identifying a specific frame of the video stream;
generating a plurality of hypotheses defining image borders within the specific frame;
selecting, by a neural network, a particular hypothesis among the plurality of hypotheses;
producing a candidate image by applying the particular hypothesis to the specific frame;
determining a value of a quality metric of the candidate image;
determining that the value of the quality metric of the candidate image exceeds one or more values of the quality metric of one or more previously processed images extracted from the video stream;
wherein the video capturing application is a zero-footprint application.
2. The method of claim 1 , further comprising performing optical character recognition of a document depicted by the candidate image.
3. The method of claim 2 , wherein the optical character recognition of the document is performed on the mobile device.
4. The method of claim 1 , further comprising cropping the candidate image.
5. The method of claim 1 , further comprising: displaying the candidate image on a screen of the mobile device.
6. The method of claim 1 , wherein receiving the video stream is performed responsive to receiving a command via a web browser control.
7. The method of claim 1 , wherein receiving the video stream is performed responsive to receiving a command via a user interface control of the mobile device.
8. A system, comprising:
a memory;
a processor, coupled to the memory, the processor configured to:
receive, by a video capturing application running on a mobile device, a video stream from a camera of the mobile device;
identify a specific frame of the video stream;
generate a plurality of hypotheses defining image borders within the specific frame;
select, by a neural network, a particular hypothesis among the plurality of hypotheses;
produce a candidate image by applying the particular hypothesis to the specific frame;
determine a value of a quality metric of the candidate image;
determine that the value of the quality metric of the candidate image exceeds one or more values of the quality metric of one or more previously processed images extracted from the video stream;
wherein the video capturing application is a zero-footprint application.
9. The system of claim 8 , wherein the processor is further configured to: perform optical character recognition of a document depicted by the candidate image.
10. The system of claim 9 , wherein the optical character recognition of the document is performed on the mobile device.
11. The system of claim 8 , wherein the processor is further configured to: crop the candidate image.
12. The system of claim 8 , wherein the processor is further configured to: display the candidate image on a screen of the mobile device.
13. The system of claim 8 , wherein receiving the video stream is performed responsive to receiving a command via a web browser control.
14. The system of claim 8 , wherein receiving the video stream is performed responsive to receiving a command via a user interface control of the mobile device.
15. A non-transitory computer-readable storage medium comprising executable instructions that, when executed by a computer system, cause the computer system to:
receive, by a video capturing application running on a mobile device, a video stream from a camera of the mobile device;
identify a specific frame of the video stream;
generate a plurality of hypotheses defining image borders within the specific frame;
select, by a neural network, a particular hypothesis among the plurality of hypotheses;
produce a candidate image by applying the particular hypothesis to the specific frame;
determine a value of a quality metric of the candidate image;
determine that the value of the quality metric of the candidate image exceeds one or more values of the quality metric of one or more previously processed images extracted from the video stream;
wherein the video capturing application is a zero-footprint application.
16. The non-transitory computer-readable storage medium of claim 15 , wherein the executable instructions further cause the computer system to: perform optical character recognition of a document depicted by the candidate image.
17. The non-transitory computer-readable storage medium of claim 16 , wherein the optical character recognition of the document is performed on the mobile device.
18. The non-transitory computer-readable storage medium of claim 15 , wherein the executable instructions further cause the computer system to: crop the candidate image.
19. The non-transitory computer-readable storage medium of claim 15 , wherein the executable instructions further cause the computer system to: display the candidate image on a screen of the mobile device.
20. The non-transitory computer-readable storage medium of claim 15 , wherein receiving the video stream is performed responsive to receiving a command via a web browser control.