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 .
@cite_27: With the proliferation of cameras on mobile devices there is an increased desire to image document pages as an alternative to scanning. However, the quality of captured document images is often lower than its scanned equivalent due to hardware limitations and stability issues. In this context, automatic assessment of the quality of captured images is useful for many applications. Although there has been a lot of work on developing computational methods and creating standard datasets for natural scene image quality assessment, until recently quality estimation of camera captured document images has not been given much attention. One traditional quality indicator for document images is the Optical Character Recognition (OCR) accuracy. In this work, we present a dataset of camera captured document images containing varying levels of focal-blur introduced manually during capture. For each image we obtained the character level OCR accuracy. Our dataset can be used to evaluate methods for predicting OCR quality of captured documents as well as enhancements. In order to make the dataset publicly and freely available, originals from two existing datasets - University of Washington dataset and Tobacco Database were selected. We present a case study with three recent methods for predicting the OCR quality of images on our dataset.
@cite_21: We describe Photo OCR, a system for text extraction from images. Our particular focus is reliable text extraction from smartphone imagery, with the goal of text recognition as a user input modality similar to speech recognition. Commercially available OCR performs poorly on this task. Recent progress in machine learning has substantially improved isolated character classification, we build on this progress by demonstrating a complete OCR system using these techniques. We also incorporate modern data center-scale distributed language modelling. Our approach is capable of recognizing text in a variety of challenging imaging conditions where traditional OCR systems fail, notably in the presence of substantial blur, low resolution, low contrast, high image noise and other distortions. It also operates with low latency, mean processing time is 600 ms per image. We evaluate our system on public benchmark datasets for text extraction and outperform all previously reported results, more than halving the error rate on multiple benchmarks. The system is currently in use in many applications at Google, and is available as a user input modality in Google Translate for Android.
@cite_6: 
@cite_6 has introduced a dataset of camera-captured document images. This dataset consist of @math pages with the text line information. In addition, ground truth text for each page is also provided. It is primarily developed for text line extraction and dewarping. It cannot be used for training of character recognition systems because there is no character, word, or line level text ground truth information available. @cite_27 has proposed a dataset containing @math images of @math documents taken with different camera settings and focus. This dataset can be used for assessing the quality of images, e.g., sharpness score. However, it cannot be used for training of OCRs on camera-captured documents, as there is no character, word, or line level text ground truth information available. @cite_21 has used a dataset of manually labeled documents which were submitted to Google for queries. However, the dataset is not publicly available, and therefore cannot be used for improving other systems.