Document image binarization is of great importance in the document image analysis and recognition pipeline since it affects further stages of the recognition process. The evaluation of a binarization method aids in studying its algorithmic behavior, as well as verifying its effectiveness, by providing qualitative and quantitative indication of its performance. This paper addresses a pixel-based binarization evaluation methodology for historical handwritten/machine-printed document images. In the proposed evaluation scheme, the recall and precision evaluation measures are properly modified using a weighting scheme that diminishes any potential evaluation bias. Additional performance metrics of the proposed evaluation scheme consist of the percentage rates of broken and missed text, false alarms, background noise, character enlargement, and merging. Several experiments conducted in comparison with other pixel-based evaluation measures demonstrate the validity of the proposed evaluation scheme.

Performance Evaluation Methodology for Historical Document Image Binarization

A system is described for creating paper documents that show images of presentation slides and bar codes that point to a multimedia recording of a presentation that has not yet occurred. An image-matching algorithm applied after a presentation determines when each slide was displayed. These time-stamps map the bar codes onto commands that control a multimedia player. We describe the system infrastructure that allows us to prepare such prescient documents and the document image-matching algorithm that enables the mapping of bar codes onto the times when slides were displayed. This provides a multimedia annotation tool that requires no electronic device at capture time.

Prescient paper: multimedia document creation with document image matching

To maintain, control and enhance the quality of document images and minimize the negative impact of degradations on various analysis and processing systems, it is critical to understand the types and sources of degradations and develop reliable methods for estimating the levels of degradations. This paper provides a brief survey of research on the topic of document image quality assessment. We first present a detailed analysis of the types and sources of document degradations. We then review techniques for document image degradation modeling. Finally, we discuss objective measures and subjective experiments that are used to characterize document image quality.

Document Image Quality Assessment: A Brief Survey

This paper proposes a deep learning approach for document image quality assessment. Given a noise corrupted document image, we estimate its quality score as a prediction of OCR accuracy. First the document image is divided into patches and non-informative patches are sifted out using Otsu's binarization technique. Second, quality scores are obtained for all selected patches using a Convolutional Neural Network (CNN), and the patch scores are averaged over the image to obtain the document score. The proposed CNN contains two layers of convolution, location blind max-min pooling, and Rectified Linear Units in the fully connected layers. Experiments on two document quality datasets show our method achieved the state of the art performance.

A deep learning approach to document image quality assessment

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

A Dataset for Quality Assessment of Camera Captured Document Images

We present a method for automatically selecting the best filter to treat poor quality printed documents using image quality assessment. We introduce five quality measures to obtain information about the quality of the images, and morphological filters to improve their quality. A training set of 370 images was used to develop the system. Experimental results on the test set show a significant improvement in the recognition rate from 73.24% using no filter at all to 93.09% after applying a filter that was automatically selected.

/pdf/automatic-filter-selection-using-image-quality-assessment-1gl1um5tpv.pdf

A. Souza

Papers

Automatic filter selection using image quality assessment