FIG. 5 shows blue Arabic handwriting on top of black text.
FIG. 6 illustrates detected regions of images and handwriting in a document according to various embodiments.
FIG. 7 is an example of detected words and phrases used to build the training set.
FIG. 8 illustrates augmented images for the training set.
FIG. 9 lists feature tokens detected in handwriting and used to build n-gram feature vector histograms.
FIG. 10 is a graph showing the document language classification accuracy improved via majority voting.
FIG. 11 illustrates, by way of example, a block diagram of an embodiment of a machine on which one or more of the methods, such as those discussed herein, can be implemented.
FIGS. 12-16 show examples of geometric features that may be detected by one or more of the disclosed embodiments.
FIG. 17 shows example data structures that may be implemented in one or more of the disclosed embodiments.
FIG. 18 is a flowchart of a process 1800 for identifying a handwriting language type.
FIG. 19 shows an example machine learning module 1900 according to some examples of the present disclosure.
FIG. 20 is a flowchart of a process 2000 for identifying a handwriting language type.
