PaleoSketch: Accurate Primitive Sketch Recognition and Beautification

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Summary

In this paper the author discusses the techniques which aid in the sketch recognition and beautification of the sketch without hampering the user ability to draw freely and naturally. It adds no constraint on the user drawing which could help in the recognition process. In this paper the author tries to recognize some primitive set of strokes.

1. Line
2. Polyline
3. Circle
4. Ellipse
5. Arc
6. Curve
7. Spiral
8. Helix
   

The system has a structure which first takes the stroke into pre-recognition routine. In the pre-recognition routine a series of graphs and values are computed. Graphs calculated are speed graph, direction graph and curvature graphs. Then the corners are calculated for the stroke. In addition to these graphs some other features are also calculated. Normalized distance between direction extremes (NDDE) and direction change ratio (DCR) is calculated. Polylines will have lower NDDE values and higher DCR values and vice versa for curves.
Then a series of tests are performed for each shape and the author in detail explains the conditions which need to be satisfied for the recognition in his paper. One thing to note here is that the author successfully recognizes shapes which are not recognized by most recognizers such as Sezgin’s recognizer. These shapes include Arc, Spiral and Helix.
If all the test of the shapes fails then the input shape is termed as complex fit. The author here defines a novel hierarchy which helps in distinguishing the shape in complex interpretation or polyline interpretation. Each primitive shape has defined weight which is calculated based on the number of corners of the primitive shapes. The cumulative weights are calculated for both types of interpretations. The interpretations with the lowest weight are taken as the interpretation of the stroke (complex wins tie).

Results:
The author analyzed a dataset of 900 shapes with three version of his own recognizers and the Sezgin’s recognizer. The Paleo (proposed recognizer), Paeo-F (Paleo without NDDE DCR features), Paleo-R (Paleo without ranking algorithm) and SSD (Sezgin’s algorithm) were used. The results with Paleo were very good and achieved an accuracy of 99.89% for correct interpretation and 98.56% for top interpretation.

Discussion

The techniques discussed in this paper are a very in-depth analysis of the shapes, which accounts for the brilliant accuracy achieved by this recognizer. It does a great job by extending the work of the Sezgin and very effectively utilizes his techniques to introduce his own novel features.
I also particularly liked the ability of this low-level recognizer to be integrated into the high-level recognition system, LADDER.