 Adaptive classifier

An adaptive classifier updates its decision rule or parameters using information acquired while processing further data. In statistical classification, this can specialize an initially broad model to the conditions of a particular stream or document. The update may use supplied labels or examples accepted under a stated trust rule; adaptation is not evidence that every accepted label is correct.

In the 2007 description of Tesseract, satisfactory first-pass words provide examples for a more document-sensitive classifier. The paper motivates this by the limited collection of fonts within a document and describes normalization differences between static and adaptive classification. It explicitly does not describe the adaptive classifier as a simple template matcher.^[ref]

A separate toy calculation illustrates parameter adaptation without claiming to reproduce that implementation. Suppose a class is represented by the mean $\mu$ of one measured feature from $n$ trusted examples. Accepting another value $x$ givesWith $n=4$, $\mu =10$ and $x=12$, the updated mean is $10.4$. A classifier using that class mean can change its later decisions as a result.

The trust rule matters as much as the arithmetic. If a wrong prediction is accepted as a label, its update can make related wrong predictions more likely. A confidence score can help select examples, but a numerical score is not automatically a calibrated probability of correctness. A useful evaluation compares a frozen baseline and the adaptive system on checked data, including the possibility of degradation as well as improvement.