Turbo recognition (TR) is a communication theory approach to the analysis of rectangular layouts, in the spirit
of Document Image Decoding. The TR algorithm, inspired by turbo decoding, is based on a generative model of
image production, in which two grammars are used simultaneously to describe structure in orthogonal (horizontal
and vertical) directions. This enables TR to strictly embody non-local constraints that cannot be taken into
account by local statistical methods. This basis in finite state grammars also allows TR to be quickly retargetable
to new domains. We illustrate some of the capabilities of TR with two examples involving realistic images. While
TR, like turbo decoding, is not guaranteed to recover the statistically optimal solution, we present an experiment
that demonstrates its ability to produce optimal or near-optimal results on a simple yet nontrivial example, the
recovery of a filled rectangle in the midst of noise. Unlike methods such as stochastic context free grammars and
exhaustive search, which are often intractable beyond small images, turbo recognition scales linearly with image
size, suggesting TR as an eÆcient yet near-optimal approach to statistical layout analysis.