  Automatic detection of font size finds many applications in the area of
intelligent OCRing and document image analysis, which has been traditionally
practiced over uncompressed documents, although in real life the documents
exist in compressed form for efficient storage and transmission. It would be
novel and intelligent if the task of font size detection could be carried out
directly from the compressed data of these documents without decompressing,
which would result in saving of considerable amount of processing time and
space. Therefore, in this paper we present a novel idea of learning and
detecting font size directly from run-length compressed text documents at line
level using simple line height features, which paves the way for intelligent
OCRing and document analysis directly from compressed documents. In the
proposed model, the given mixed-case text documents of different font size are
segmented into compressed text lines and the features extracted such as line
height and ascender height are used to capture the pattern of font size in the
form of a regression line, using which the automatic detection of font size is
done during the recognition stage. The method is experimented with a dataset of
50 compressed documents consisting of 780 text lines of single font size and
375 text lines of mixed font size resulting in an overall accuracy of 99.67%.
