paper presents an overview of image acquisition and post-processing technologies
developed in the interdisciplinary project The Enigma of the Sinaitic
Glagolitic Tradition. A multi-spectral image recording system using a
combination of LED illumination and spectral filtering is described. The
possibilities of two different methods of Blind Source Separation, namely
Principal Component Analysis (PCA) and Independent Component Analysis (ICA),
applied on palimpsest documents are discussed in combination with multispectral
input information. We also introduce a new approach to Optical Character
Recognition (OCR) that is independent of preceding segmentation of fore- and
background, but is based upon local descriptor information. Finally we present
a handy and fast image viewer program specialized on multispectral and low contrast
In the project The Enigma of the Sinaitic Glagolitic Tradition (ASF
19608-G12), the follow-up project of The Sinaitic Glagolitic Sacramentary
(Euchologium) Fragments (ASF 23133), an interdisciplinary project group of
philologists and computational imaging researchers specialize in developing
computational means for digitization, image enhancement, text decipherment, and
visualization of historical manuscripts to facilitate philological investigations.
MSI provides substantial improvement of the legibility of manuscripts
that are degraded and barely decipherable under visible light (VIS) (cf. [Knox,
2008]). While the human eye responds to wavelengths ranging between approximately
400 and 700nm, our imaging system allows also for an image acquisition beyond
the visible spectrum. Particularly, we make use of three imaging techniques: UltraViolet
(UV) reflectography for reflected UV light, whereby the reflected
VIS light is filtered out, UV fluorescence, where the incident UV light
is filtered out and only the fluorescence in the VIS range is captured, and InfraRed
(IR) reflectography, where the VIS part of the light spectrum is filtered
out, so that the resulting images exhibit solely the light reflected in the IR
We use a Hamamatsu C9300-124 gray scale camera with a spectral response
from 330 to 1000 nm. In order to select a certain spectral range, a filter
wheel is used that is mounted in front of the camera with 8 different optical
filters (cf. Table 1)/Additionally, a Nikon D2Xs SLR camera is used for
RGB and UV fluorescence photographs.
VIS-IR / UV fluorescence
Table 1: Optical filters fitted in the filter wheel. SP is a
short pass, LP a long pass filter, and BP a band pass filter. The LP 400 filter
allows for VIS-IR and UV fluorescence photographs, because white light
and UV illumination are used in combination.
The lighting system has improved in the course of the projects. The
first setup included UV and halogen lamps in combination with the optical
filters. In the current acquisition setup two LED panels with 13 different
narrow spectral bands (cf. Fig. 1) replace the former light sources.
Fig. 1. Spectra of the LED panels
Additionally, four white light
LED panels are used for the RGB photographs, because LED lighting reduces heat
and stress on the manuscripts [Christens-Barry, 2012] and makes subsequent image registration steps obsolete, except for UV reflectography
and fluorescence photographs [Lettner et al., 2007].
The schematic illustration of the current acquisition setup (cf. Fig.
2) shows an object placed on a plate, which is mounted on a linear unit
for automated movement between both cameras. Two diffusers in front of the LED
panels guarantee a uniform light distribution.
Fig. 2. Schematic illustration of the image acquisition
Blind Source Separation
This procedure, the separation of mixed signals, is tailored especially
for palimpsest images and images of degraded manuscripts (cf. the
multi-spectral sample Fig. 3).
Although the underwriting is visible under UV illumination, it is still
barely legible due to low contrast to the background and additional background
noise. Both methods to enhance the older text belong to the category of
blind source separation, which applies statistical assumptions and blind demixing. In our case, the sources to be separated
are the two writing-layers, mold, parchment, etc. The mixtures are given in the
form of multi-spectral images.
The first technique, Principal Component Analysis (PCA), is a
statistical method to transform correlated into uncorrelated variables. The second, Independent Component Analysis (ICA),
other than PCA, transforms statistically independent signals (cf. [Hyvärinen et al., 2001]).
Fig. 3. Palimpsest illuminated at different wavelengths
Experimental results show that contrast enhancement could be
successfully executed by both source separating techniques, but the PCA
approach was often defeated by the ICA approach. Still there are cases, where
the ICA algorithm could not enhance the contrast of the underwriting. In Fig. 4 we can see that the writing in the PCA output (Fig. 4 last two rows, left) has lower background contrast than the ICA output
(last two rows, right). Furthermore, the red initials (first row, left) are visible
in the PCA outputs, whereas the ICA algorithm identified them correctly as a different
Figure 4 last two rows, left) has lower background contrast than the ICA
output (last two rows, right). Furthermore, the red initials (first row, left)
are visible in the PCA outputs, whereas the ICA algorithm identified them
correctly as a different source.
Fig. 4. 1st row: Nikon camera; left:: white light; right:: UV
fluorescence image. 2nd/3rd row:
left:: PCA, right::
Optical Character Recognition (OCR)
Contrary to other state-of-the-art techniques [Vinciarelli, 2002] the here described OCR system
does not require a preceding binarization or character segmentation step, which
often is an impossible challenge [Gatos et al., 2006] for low contrast,
damaged, or partially faded-out manuscripts (cf. Fig. 5 (b) [Sauvola et al., 2000]). The output of the proposed OCR algorithm
is shown in Fig. 5 (c).
The OCR algorithm is based on local descriptors which describe the local
neighborhood around an interest point. Interest points are prominent image
points; in the proposed algorithm they are found by applying the
Difference-of-Gaussians approach. In order to calculate reliable descriptors,
the system makes use of the Scale Invariant Feature Transform (SIFT) [Lowe,
2004]. One advantage of the SIFT approach is its invariance to certain
transformations, including translation, rotation, and scaling.
Then the local descriptors are classified by applying a multi-kernel
Support Vector Machine (SVM). This SVM is trained by using 20 different
training images per character. The output of the classification for each local
descriptor is a probability histogram for character classes. Afterwards,
characters are localized by clustering the interest points by the k-means
clustering algorithm, which assigns each character to a certain cluster. In a
final classification step, the class probabilities of each descriptor in a
cluster are considered. The probabilities are accumulated in a voting step,
assigning the most probable character to the relevant cluster.
Fig. 5. Binarization and OCR applied on a portion of a faded Glagolitic
manuscript: (a) Input image, (b) binarization result of [Sauvola et al., 2000],
(c) characters recognized by the proposed system.
Our experiments were executed on 1055 characters
belonging to 10 different classes which were divided into two different sets:
regular and degraded characters. The results are given in Table 2.
Table 2: Performance of the proposed OCR system.
It can be seen that the performance gained on degraded, faded-out characters
is considerably inferior to the results achieved on regular text (cf. last
column of Table 2).
Nomacs Image Lounge ― Image Viewer
Long-term experiences with multispectral images and low contrast
pictures of damaged manuscripts have led to the development of a specialized
image viewing program ― the Nomacs viewer. It is small, fast, able to
handle the most common image formats and runs on all major operating systems. This
freeware system under GNU Public License v3 [Diem et al., 2011/12] is currently
available in three languages: English, German, and Russian.
The Nomacs viewer counters several disadvantages of other programs
on the market both for philological investigations, as well as for
computational examinations of historical manuscripts. Its key features for the
purposes of multispectral image visualization and manuscript including
palimpsest research are:
multiple instances of an image (e.g. different spectra) including zooming and
panning of all synchronized images, as well as synchronized moving to the
pseudocolor function for manipulating the color contrast of low contrast
writing or palimpsest text (cf. Figure 6).
Fig. 6. High contrast false color image
of metadata and exif information
of two or more images (with adjustable opacity)
and sending multiple instances in the LAN
The research was funded by the Austrian Science Fund (FWF): P23133. We
would like to say special thanks to St. Catherine's Monastery, Mount Sinai, for
the kind access to the research objects
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