3. A computing device comprising: at least one processor; and a memory storing instructions which when executed the at least one processor to: identify edges in an image using an edge detection filter; identify a plurality of edge points of the image using the identified edges; determine respective first edge transition widths at each edge point of the plurality of edge points; determine a first statistic based on the first edge transition widths; determine a sharpness of the image based on the first statistic; process the image to identify text; determine, from the plurality of edge points, a set of edge points that are proximate to the text; determine respective second edge transition widths for each edge point of the set of edge points; determine a second statistic using the respective second edge transition widths; and perform optical character recognition on the text based on the second statistic.
3. A computing device comprising: at least one processor; and a memory storing instructions which when executed the at least one processor to: identify edges in an image using an edge detection filter; identify a plurality of edge points of the image using the identified edges; determine respective first edge transition widths at each edge point of the plurality of edge points; determine a first statistic based on the first edge transition widths; determine a sharpness of the image based on the first statistic; process the image to identify text; determine, from the plurality of edge points, a set of edge points that are proximate to the text; determine respective second edge transition widths for each edge point of the set of edge points; determine a second statistic using the respective second edge transition widths; and perform optical character recognition on the text based on the second statistic. 10. The computing device of claim 3 , wherein the instructions to determine the sharpness of the image comprise instructions to: input the first statistic into a trained classifier; and determine the sharpness based on an output of the trained classifier.