  The contribution of this paper is fourfold. The first contribution is a
novel, generic method for automatic ground truth generation of camera-captured
document images (books, magazines, articles, invoices, etc.). It enables us to
build large-scale (i.e., millions of images) labeled camera-captured/scanned
documents datasets, without any human intervention. The method is generic,
language independent and can be used for generation of labeled documents
datasets (both scanned and cameracaptured) in any cursive and non-cursive
language, e.g., English, Russian, Arabic, Urdu, etc. To assess the
effectiveness of the presented method, two different datasets in English and
Russian are generated using the presented method. Evaluation of samples from
the two datasets shows that 99:98% of the images were correctly labeled. The
second contribution is a large dataset (called C3Wi) of camera-captured
characters and words images, comprising 1 million word images (10 million
character images), captured in a real camera-based acquisition. This dataset
can be used for training as well as testing of character recognition systems on
camera-captured documents. The third contribution is a novel method for the
recognition of cameracaptured document images. The proposed method is based on
Long Short-Term Memory and outperforms the state-of-the-art methods for camera
based OCRs. As a fourth contribution, various benchmark tests are performed to
uncover the behavior of commercial (ABBYY), open source (Tesseract), and the
presented camera-based OCR using the presented C3Wi dataset. Evaluation results
reveal that the existing OCRs, which already get very high accuracies on
scanned documents, have limited performance on camera-captured document images;
where ABBYY has an accuracy of 75%, Tesseract an accuracy of 50.22%, while the
presented character recognition system has an accuracy of 95.10%.
