Data entry system with drawing recognition

1. An electronic device comprising:

a memory storing an image for display to a user;

a shape recognition component which receives drawing input as a result of the user drawing at a location of the displayed image;

a semantic segmentation component configured to carry out image analysis of the image to compute one or more object classes of objects depicted at the location in the image; and wherein

the shape recognition component is configured to recognize the drawing input as being in a computed graphical item class of a plurality of pre-specified graphical item classes by analyzing a shape of the drawing input and by using information about the computed object classes of the one or more objects depicted at the drawing input location in the image; and

a processor configured to offer at least one graphical item of the computed graphical item class for input to the electronic device.

2. The electronic device of claim 1 wherein the location in the image comprises a bounding box computed to bound the drawing input.

3. The electronic device of claim 1 wherein the location in the image comprises coordinates of the drawing input in the image.

4. The electronic device of claim 1 wherein the processor is configured such that, when user input is received selecting the offered graphical item, the offered graphical item is inserted into the image at the location.

5. The electronic device of claim 1 wherein the processor is configured such that, when user input is received selecting the offered graphical item, the offered graphical item is inserted into the image at the location so as to replace the drawing input.

6. The electronic device of claim 1 wherein the processor is configured to retrieve the at least one graphical item of the computed graphical item class by sending an information retrieval request to a graphical item store.

7. The electronic device of claim 1 wherein the semantic segmentation component is a trained machine learning system selected from one or more of: a random decision forest, a neural network, a support vector machine.

8. The electronic device of claim 1 wherein the shape recognition component is a trained machine learning system selected from one or more of: a random decision forest, a neural network, a support vector machine.

9. The electronic device of claim 8 wherein the shape recognition component has been trained using training data comprising pairs, each pair comprising: semantic segmentation data, location data, drawing input and a shape of a known class from a plurality of specified classes of shape.

10. The electronic device of claim 1 wherein the image is a digital photograph or video.

11. The electronic device of claim 1 being a wearable computer.

12. The electronic device of claim 1 being a hand held computing device.

13. A computer-implemented method of data entry comprising:

storing at a memory an image for display to a user;

receiving drawing input as a result of the user drawing at a location of the displayed image;

computing image analysis of the image to compute one or more object classes of objects depicted at the location in the image;

recognizing the drawing input as being in a computed graphical item class of a plurality of pre-specified graphical item classes by analyzing a shape of the drawing input and by using information about the computed object classes of the one or more objects depicted at the drawing input location in the image; and

offering at least one graphical item of the computed graphical item class for input to the electronic device.

14. The method of claim 13 comprising computing a bounding box of the drawing input as the location in the image.

15. The method of claim 13 comprising obtaining coordinates of the drawing input in the image as the location.

16. The method of claim 13 comprising replacing the drawing input on the image by the offered at least one graphical item in response to user input selecting the at least one graphical item.

17. The method of claim 16 comprising adjusting a color balance of the graphical item to match a color balance of the image before the replacing of the drawing input.

18. The method of claim 13 comprising retrieve the at least one graphical item of the computed graphical item class by sending an information retrieval request to a graphical item store.

19. The method of claim 13 comprising recognizing the drawing input using a shape recognizer comprising a machine learning component, and training the shape recognizer using pairs of data, each pair comprising semantic segmentation data, location data, drawing input and a shape of a known class from a plurality of specified classes of shape.

20. One or more computer storage media with device-executable instructions that, when executed by a computing system, direct the computing system to perform operations comprising:

storing at a memory a video for display to a user;

receiving drawing input as a result of the user drawing at a location of the displayed video;

computing image analysis of the video to compute one or more object classes of objects depicted at the location in the video;

recognizing the drawing input as being in a computed graphical item class of a plurality of pre-specified graphical item classes by analyzing a shape of the drawing input and by using information about the computed object classes of the one or more objects depicted at the drawing input location in the video; and

offering at least one graphical item of the computed graphical item class for input to the electronic device.