Content-level Annotation of Large Collection of Printed Document Images

Abstract

A large annotated corpus is critical to the development of robust optical character recognizers (OCRs). However, creation of annotated corpora is a tedious task. It is laborious, especially when the annotation is at the character level. In this paper, we propose an efficient hierarchical approach for annotation of large collection of printed document images. We align document images with independently keyed-in text. The method is model-driven and is intended to annotate large collection of documents, scanned in three different resolutions, at character level. We employ an XML representation for storage of the annotation information. APIs are provided for access at content level for easy use in training and evaluation of OCRs and other document understanding tasks.