Conventional approaches to combining classifiers improve accuracy at the cost of increased processing. We propose a novel search based approach to automatically combine multiple classifiers in a cascade to obtain the desired tradeoff between classification speed and classification accuracy.

The search procedure only updates the rejection thresholds (one for each constituent classier) in the cascade, consequently no new classifiers are added and no training is necessary. Heuristic methods for finding approximate solutions and branch-and-bound search for optimal solutions are being explored. On handwritten/print character recognition such cascade classifiers can generate 5-10x speedup in processing times.