  Detection and recognition of text from scans and other images, commonly
denoted as Optical Character Recognition (OCR), is a widely used form of
automated document processing with a number of methods available. Yet OCR
systems still do not achieve 100% accuracy, requiring human corrections in
applications where correct readout is essential. Advances in machine learning
enabled even more challenging scenarios of text detection and recognition
"in-the-wild" - such as detecting text on objects from photographs of complex
scenes. While the state-of-the-art methods for in-the-wild text recognition are
typically evaluated on complex scenes, their performance in the domain of
documents is typically not published, and a comprehensive comparison with
methods for document OCR is missing. This paper compares several methods
designed for in-the-wild text recognition and for document text recognition,
and provides their evaluation on the domain of structured documents. The
results suggest that state-of-the-art methods originally proposed for
in-the-wild text detection also achieve competitive results on document text
detection, outperforming available OCR methods. We argue that the application
of document OCR should not be omitted in evaluation of text detection and
recognition methods.
