Elisa H. Barney Smith

Text degradations and OCR training

Printing and scanning of text documents introduces degradations to the characters which can be modeled. Interestingly, certain combinations of the parameters that govern the degradations introduced by the printing and scanning process affect characters in such a way that the degraded characters have a similar appearance, while other degradations leave the characters with an appearance that is very different. It is well known that (generally speaking), a test set that more closely matches a training set is recognized with higher accuracy than one that matches the training set less well. Likewise, classifiers tend to perform better on data sets that have lower variance. This paper explores an analytical method that uses a formal printer/scanner degradation model to identify the similarity between groups of degraded characters. This similarity is shown to improve the recognition accuracy of a classifier through model directed choice of training set data.

Enhancement of historical printed document images by combining Total Variation regularization and Non-local Means filtering

This paper proposes a novel method for document enhancement which combines two recent powerful noise-reduction steps. The first step is based on the Total Variation framework. It flattens background grey-levels and produces an intermediate image where background noise is considerably reduced. This image is used as a mask to produce an image with a cleaner background while keeping character details. The second step is applied to the cleaner image and consists of a filter based on Non-local Means: character edges are smoothed by searching for similar patch images in pixel neighborhoods. The document images to be enhanced are real historical printed documents from several periods which include several defects in their background and on character edges. These defects result from scanning, paper aging and bleed-through. The proposed method enhances document images by combining the Total Variation and the Non-local Means techniques in order to improve OCR recognition. The method is shown to be more powerful than when these techniques are used alone and than other enhancement methods.

An analysis of binarization ground truthing

The accuracy of a binarization algorithm is often calculated relative to a ground truth image. Except for synthetically generated images, no ground truth image exists. Evaluating binarization on real images is preferred. The ground truthing between and among different operators is compared. Four direct metrics were used. The variability of the results of five different automatic binarization algorithms were compared to that of manual ground truth results. Significant variability in the ground truth results was found.

Characterization of image degradation caused by scanning

Abstract A single parameter value that represents the difference between the original and the digitized characters is determined from a binary scan of a test chart. It represents the combined effect of the point spread function diameter and the binary intensity threshold and is used to generate synthetic characters that are similar to characters scanned under the same conditions as the test chart.

PSF Estimation by Gradient Descent Fit to the ESF

Calibration of scanners and cameras usually involves measuring the point spread function (PSF). When edge data is used to measure the PSF, the differentiation step amplifies the noise. A parametric fit of the functional form of the edge spread function (ESF) directly to the measured edge data is proposed to eliminate this. Experiments used to test this method show that the Cauchy functional form fits better than the Gaussian or other forms tried. The effect of using a functional form of the PSF that differs from the true PSF is explored by considering bilevel images formed by thresholding. The amount of mismatch seen can be related to the difference between the respective kurtosis factors.Calibration of scanners and cameras usually involves measuring the point spread function (PSF). When edge data is used to measure the PSF, the differentiation step amplifies the noise. A parametric fit of the functional form of the edge spread function (ESF) directly to the measured edge data is proposed to eliminate this. Experiments used to test this method show that the Cauchy functional form fits better than the Gaussian or other forms tried. The effect of using a functional form of the PSF that differs from the true PSF is explored by considering bilevel images formed by thresholding. The amount of mismatch seen can be related to the difference between the respective kurtosis factors.

Scanner parameter estimation using bilevel scans of star charts

Scanning a high-contrast image in bilevel mode results in image degradation. This is caused by two primary effects: blurring and thresholding. This paper expands on a method of estimating a joint distortion parameter called the edge spread, from a star sector test chart in order to calculate the values of the point spread function width and binarization threshold. This theory is also described for variations in the source pattern which can represent degradations caused by repetition of the bilevel process as would be seen in printing then scanning, or in repeated photocopying. Estimation results are shown for the basic and extended cases.

A Document Analysis System for Supporting Electronic Voting Research

As a result of well-publicized security concerns with direct recording electronic (DRE) voting, there is a growing call for systems that employ some form of paper artifact to provide a verifiable physical record of a voters choices. In this paper, we present a system we are developing to support a multi-institution, cross-disciplinary research project examining issues that arise when paper ballots are used in elections. We survey the motivating factors behind our work, discuss the special constraints raised in processing ballots as opposed to more general document images, and describe the current status of our system.

Estimating scanning characteristics from corners in bilevel images

Degradations that occur during scanning can cause errors in Optical Character Recognition (OCR). Scans made in bilevel mode (no grey scale) from high contrast source patterns are the input to the estimation processes. Two scanner system parameters are estimated from bilevel scans using models of the scanning process and bilevel source patterns. The scanner’s point spread function (PSF) width and the binarization threshold are estimated by using corner features in the scanned images. These estimation algorithms were tested in simulation and with scanned test patterns. The resulting estimates are close in value to what is expected based on grey-level analysis. The results of estimation are used to produce synthetically scanned characters that in most cases bear a strong resemblance to the characters scanned on the scanner at the same settings as the test pattern used for estimation.Degradations that occur during scanning can cause errors in Optical Character Recognition (OCR). Scans made in bilevel mode (no gray scale) from high contrast source patterns are the input to the estimation processes. Two scanner system parameters are estimated from bilevel scans using models of the scanning process and bilevel source patterns. The scanners point spread function (PSF) width and the binarization threshold are estimated by using corner features in the scanned images. These estimation algorithms were tested in simulation and with scanned test patterns. The resulting estimates are close in value to what is expected based on gray-level analysis. The results of estimation are used to produce synthetically scanned characters that in most cases bear a strong resemblance to the characters scanned on the scanner at the same settings as the test pattern used for estimation.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Pre-Processing of Degraded Printed Documents by Non-local Means and Total Variation

We compare in this study two image restoration approaches for the pre-processing of printed documents:namely the Non-local Means filter and a total variation minimization approach. We apply these two approaches to printed document sets from various periods,and we evaluate their effectiveness through character recognition performance using an open source OCR. Our results show that for each document set, one or both pre-processing methods improve character recog-nition accuracy over recognition without preprocessing. Higher accuracies are obtained with Non-local Means when characters have a low level of degradation since they can be restored by similar neighboring parts of non-degraded characters. The Total Variation approach is more effective when characters are highly degraded and can only be restored through modeling instead of using neighboring data.

Style-Based Ballot Mark Recognition

The push toward voting via hand-marked paper ballots has focused attention on the limitations of current optical scan systems. Discrepancies between human and machine interpretations of ballot markings can lead to a loss of trust in the election process. In this paper, a style-based approach to ballot recognition is proposed in which marks are recognized collectively rather than in isolation. The consistency of a voters style is leveraged to improve the overall accuracy of the system. We compare style-based recognition to various kinds of singlet classifiers and show that it outperforms them by a substantial margin.