Reading #13 Ink Features for diagram recognition

Summary

 This paper aims to analysis the importance of many ink features and uses them to distinguish test against shapes. It first review some techniques used in the area of sketch recognition: bottom-up approaches like Rubine’s and template matching, top-down and combination of these two kinds of approaches.

Then, this paper selects forty-six features and evaluates their significance based on formal statistical analysis. They use statistical partitioning techniques to generate a decision tree as below

This paper then uses these features to build a text/shape divider and compare it with other two dividers. The result show it has an improvement both on classify shape and text.

Discussion

Distinguish text from shape is very important in many situation. In addition, it is very interesting. This paper finds out 8 most useful features to distinguish text from shape. However, in the paper of Doctor Hammond, only the feature of entropy is used and result is also good. The divider introduced in this paper of defining some threshold is similar with what I did in project two.