Human Reading and the Curse of Dimensionality
Gale L. Martin · 1995
Whereas optical character recognition (OCR) systems learn to clas-sify single characters; people learn to classify long character strings in parallel, within a single fixation. This difference is surprising because high dimensionality is associated with poor classification learning. This paper suggests that the human reading system avoids these problems because the number of to-be-classified im-ages is reduced by consistent and optimal eye fixation positions, and by character sequence regularities. An interesting difference exists between human reading and optical character recog-nition (OCR) systems. The input/output dimensionality of character classification in human reading is much greater than that for OCR systems (see Figure 1). OCR systems classify one character at time; while the human reading system classifies as many as 8-13 characters per eye fixation (Rayner, 1979) and within a fixation,