Ink Features for Diagram Recognition.

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Summary

This paper discusses technique to separate text from images in a free-hand drawn sketch system. Technique used in this paper is to use a classification tree after identifying a set of features which can help in distinguishing between text and shape. The classification tree mentioned in this paper is a binary tree. Each feature is node in the tree and this feature decides to split the input into a text, shape or sub tree.

The most important features are put at the highest node in the classification tree. To determine if the feature is more important or not, a simple test on all features separately in conducted. The feature which most accurately classifies a stroke into shape or text is considered as an important feature and consequently takes a higher position on the classification tree. Some of the proposed features are bounding box, total angle, distance from last stroke, distance to next stroke, speed to next stroke, amount of ink inside, perimeter to area, and time till next stroke.

The system is compared with Microsoft Ink SDK and InkKit. The overall misclassification on shape and text are fairly low as compared to the other techniques for the algorithm presented by the author. The misclassification for shape is 42.1% and for text is 21.4% on testing dataset.

Discussion

This paper addresses an important problem in sketch recognition of separating text vs shape. The reason which I feel that most sketch recognition systems are not widely used because they cannot appropriately distinguish between text and shape.