  We report upon the results of a research and prototype building project
\emph{Worldly~OCR} dedicated to developing new, more accurate image-to-text
conversion software for several languages and writing systems. These include
the cursive scripts Farsi and Pashto, and Latin cursive scripts. We also
describe approaches geared towards Traditional Chinese, which is non-cursive,
but features an extremely large character set of 65,000 characters. Our
methodology is based on Machine Learning, especially Deep Learning, and Data
Science, and is directed towards vast quantities of original documents,
exceeding a billion pages. The target audience of this paper is a general
audience with interest in Digital Humanities or in retrieval of accurate
full-text and metadata from digital images.
