Sayre's paradox

[3] It is relatively easy to design automated systems capable of recognizing words inscribed in a printed format.

In cases of ambiguity, probable letter sequences can be compared with a selection of properly spelled words in that language (called a lexicon).

There is no way a cursive writing recognition system employing standard template-matching techniques can do both simultaneously.

[4] This procedure can increase the probability of a correct match with a letter template, resulting in an incremental improvement in the success rate of the system.

Since improvement of this sort still depends on accurate segmentation, however, it remains subject to the limitations of Sayre's Paradox.

[1] Segmentation is accurate to the extent that it matches distinctions among letters in the actual inscriptions presented to the system for recognition (the input data).

Processing these “implicit parts” to achieve eventual word identification requires specific statistical procedures involving hidden Markov models (HMM).