50120130405033

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-6367(Print),
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
ISSN 0976 - 6375(Online), Volume 4, Issue 5, September - October (2013), © IAEME

TECHNOLOGY (IJCET)

ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 5, September – October (2013), pp. 285-291
© IAEME: www.iaeme.com/ijcet.asp
Journal Impact Factor (2013): 6.1302 (Calculated by GISI)
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IJCET
©IAEME

EFFECTIVE THRESHOLDING OF ANCIENT DEGRADED MANUSCRIPT
FOLIO IMAGES
Lalit Saxena
Department of Computer Science, University of Mumbai, Mumbai, India

ABSTRACT
Thresholding is an essential procedure used in image segmentation and binarization
applications. In this paper, segmentation methods applied on document images for separating the text
from background presents pure binarization and filtering combined with image processing
algorithms. This paper describes a contrast based thresholding method for old degraded manuscript
images. It is an approach for degraded manuscript and document images by introducing an
estimation of the threshold value. This technique effectively segments the texts from badly degraded
document background. The method is suitable for segmentation of document images with complex
and uneasy background having unreadable text. Proposed method performs segmentation using
contrast estimating a threshold and exhaustively uses discrete gray level values. The proposed
method broadly evaluated on more than 100 degraded manuscript images. The result shows the
readable text in the improved images produced by the proposed method. Experiments confirm the
effectiveness of the proposed method compared to standard thresholding methods. In research, the
proposed method produced better results than standard thresholding methods for original manuscript
images.
Keywords: Degradation, Folio Images, Manuscripts, Segmentation, Thresholding.
I.

INTRODUCTION

Thresholding is a rapid and precise procedure of segmentation of color and gray scale
images. It is a sufficiently accurate and high processing speed segmentation approach to
monochrome image. Over the years several thresholding techniques developed; but all of them aimed
to have a generic approach to deal with different kinds of documents. There are two kinds of
thresholding methods: global and local thresholding. Global thresholding algorithms use a discrete
threshold for an image. These are intending to locate a discrete threshold to remove all pixels from
the image background, while preserving all possible pixels in foreground. When there is a good
285


separation between background and foreground, global thresholding algorithms achieve high
efficacy. For manuscript folio images, complex backgrounds and weak image foregrounds (many
foreground pixels cover gray values close to those of some background pixels) toughens this
procedure. In such cases, it is not possible to find a single threshold that separates the foreground
from the image background. Thus, if this approach decides to binarize all the background pixels, then
it also binarizes some of the foreground pixels. This results in broken texts that it not preserved the
connectivity of strokes of the characters. On the other hand, local approaches estimate a separate
threshold for background and foreground, on the basis of pixel neighborhoods. However, many
document images have complex backgrounds that make the separation not so simple. Local or
adaptive thresholding presents a better performance when treating documents with complex
backgrounds. By contrast, adaptive thresholding methods fail in preserving stroke connectivity.
II.

STATE OF THE ART

Despite of all the efforts made to restore degraded document images, recovery of the texts
requires more efforts. The algorithm proposed by [1], initially binarizes the image using global
method, and later invokes a comparable refinement method on each connected component to
generate the absolute precise binary image. The document degradations happened because of
shadows, non-uniform illumination, low contrast, large signal-dependent noise, smear and strain,
handled by an approach developed by [2]. A nonparametric optimal threshold selection for image
segmentation maximizing the separation of the gray level classes suggested by [3]. [4] proposed
maximum entropy algorithm using probability distributions separating an image into objects and
background on the basis of gray levels histogram. The method in [5] creates a threshold surface to
find exact object boundaries for local threshold values using a gradient map of the image. A new
concept about global thresholding proposed by [6] that separates an image into three regions, i.e.,
foreground, background, and a fuzzy area. Using multi-scale texture segmentation and spatial
cohesion constraints to detect and extract text in images proposed by [7]. [8] introduced a method to
binarize degraded and poor quality gray scale images having signal-dependent noise using logical
adaptive thresholding. The method in [9], considered an image as a collection of subcomponents of
text, background and picture for adaptive document image binarization. [6] proposed new
thresholding technique and compared it against some existing algorithms. The experiment done
using simple and complex images of postal envelopes by [7] used a multi-stage global thresholding
approach followed by a local spatial thresholding. An image binarization method using [10] for low
quality historical documents proposed by [11], calculates background surface by interpolating
neighboring pixel intensities. A detailed survey on image thresholding methods with comparisons
and categorization given by [12] and [13]. [14] introduced a local feature thresholding decompose
algorithm, document sub regions using quad-tree decomposition and compared global and local
thresholding techniques for degraded historical documents images. Considering that the text contains
only 10% of the document image for binarization presented by [15]. [16] proposed a Kohonen
adaptive neural network system for the binarization of normal and degraded documents for
visualization and recognition of text characters.
III.

PROPOSED AND OTHER METHODS

This paper describes an effective thresholding method for binarization of heavily degraded
and poor quality gray scale manuscript images. This method can deal with complex signal-dependent
noise and variable background intensity caused by non-uniform illumination, shadow, smear or
smudge and very low contrast images. The outcome binary image has no observable loss of useful
texts. The proposed method extracts the binary image adaptively from the degraded gray scale
286


document image with complex and inhomogeneous background. It estimates the value of threshold
using contrast and gray values of the pixels. This method can threshold various poor quality gray
scale document images without the need of any prior knowledge of the document. And it not requires
any fine-tuning of parameters and also without taking into account characters geometric features. It
keeps information accurately without over connected and broken strokes of the characters, and thus,
has a wider range of applications. The block diagram of the proposed method is provided for precise
understanding.

Original Manuscripts

Manuscripts images

Gray scale conversion

Threshold calculation
Adaptive threshold
Enhanced image
Block diagram of the proposed method
•

Block 1: Original manuscripts: The original manuscripts collected in its native form without
any external alterations. This is exceptional to possible deterioration removal.

•

Block 2: Manuscripts images: Camera with high resolution (this work used 14mega pixels) for
clarity and format readable to latest computer.

•

Block 3: Gray scale conversion: Gray scale image of the color image produced to reduce the
pixel processing complexity, since color image has three values; R, G, B.

•

Block 4: Threshold calculation: Threshold calculation involves gray scale values of the image
pixels intensity, contrast used to understand the difference between foreground text and
background.

•

Block 5: Adaptive thresholding: This adaptively thresholds gray scale image to binarized
image.

•

Block 6: Enhanced image: The enhanced image with clear text, easy readability is the output of
the proposed method.

287


1. Proposed Method: It is obvious that a fixed value of the threshold estimation ܶሺ‫ݕ ,ݔ‬ሻ ൌ ܿ‫.ݐݏ݊݋‬
cannot yield satisfactory binarization results for images obtained under non-uniform illumination or
with a non-uniform background. The proposed method calculates the local threshold value based in
the mean value of the minimum and maximum intensities of pixels within a window [17]. If the
window is centered at the pixel ሺ‫ݕ ,ݔ‬ሻ the threshold for ݂ሺ‫ݕ ,ݔ‬ሻ is defined by:
ܶሺ‫ݕ ,ݔ‬ሻ ൌ

ܶ௠௔௫ ൅ ܶ௠௜௡
2

where ܶ௠௔௫ and ܶ௠௜௡ are the maximum and minimum intensity of the pixels in the window. This
estimation of threshold value works correctly only when the contrast is sufficiently high. Also, the
contrast is defined as ‫ ܥ‬ሺ‫ݕ ,ݔ‬ሻ ൌ ܶ௠௔௫ െ ܶ௠௜௡ [18]. It suggests that if the contrast is less than this
value the pixels within the window will be assigned to background or foreground depending on the
window. The proposed method is dependent on the size ܰ of the window defined by ܰ െ ܾ‫ ݕ‬െ ܰ.
2. Otsu's method: Suggested a discriminant analysis method for thresholding of the images. It is a
formal pattern recognition procedure in which a criterion function used as a measure of statistical
separation between classes. Calculations done for the two classes of intensity values (foreground and
ଶ
ଶ
background) separated by an intensity threshold. The criterion function used here is ߪ஻௜ ⁄ߪ் for every
ଶ
ଶ
intensity, ݅ ൌ 0, … , ‫ ܫ‬െ 1, where ߪ஻௜ is the between-class variance and ߪ் is the total variance. The
intensity that maximizes this function said to be the optimal threshold.
3. Niblack's method: This method calculates the local mean and local standard deviation [10] of the
image pixels in the window. It calculates the threshold value at pixel (x,y) by:
ܶሺ‫ݕ ,ݔ‬ሻ ൌ ݉ሺ‫ݕ ,ݔ‬ሻ ൅ ݇. ‫ݏ‬ሺ‫ݕ ,ݔ‬ሻ
where ݉ሺ‫ݕ ,ݔ‬ሻ and ‫ݏ‬ሺ‫ݕ ,ݔ‬ሻ are the mean and the standard deviation of a local area respectively. The
size of the window must be large enough to suppress the noise in the image, but also small enough to
preserve local details of the image. A window size 15 െ ܾ‫ ݕ‬െ 15 works efficiently. The value of k
used to adjust the percentage of total pixels that belong to foreground object especially in the
boundaries of the object. A value of ሾെ0.2ሿ produces objects separated well enough from
background.
4. Sauvola's method: In this binarization method, the threshold ܶሺ‫ݕ ,ݔ‬ሻ calculated using the mean
݉ሺ‫ݕ ,ݔ‬ሻ and standard deviation ‫ݏ‬ሺ‫ݕ ,ݔ‬ሻ of the pixel intensities in a window centered around the pixel
ሺ‫ݕ ,ݔ‬ሻ:
‫ݏ‬ሺ‫ݕ ,ݔ‬ሻ
ܶሺ‫ݕ ,ݔ‬ሻ ൌ ݉ሺ‫ݕ ,ݔ‬ሻ ൅ ൤1 ൅ ݇ ൬
൰൨ െ 1
ܴ
where ܴ is the maximum value of the standard deviation (ܴ ൌ 128 for a gray scale document), and
݇ is a parameter which takes positive values in the range ሾ0.2 െ 0.5ሿ. The local mean ݉ሺ‫ݕ ,ݔ‬ሻ and
standard deviation ‫ݏ‬ሺ‫ݕ ,ݔ‬ሻ adapt the value of the threshold according to the contrast in the local
neighborhood of the pixel. When there is high contrast in some region of the image, ‫ݏ‬ሺ‫ݕ ,ݔ‬ሻ ൎ ܴ
which results in ܶሺ‫ݕ ,ݔ‬ሻ ൎ ݉ሺ‫ݕ ,ݔ‬ሻ.

288

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976
0976-6367(Print),

IV.

EXPERIMENTAL RESULTS

While global thresholding algorithms are not good enough to treat complex backgrounds and
local approaches do not preserve stroke connectivity (critical for digitization and preservation of
manuscripts), the proposed approach successfully removes the background, yet keeping stroke
connectivity untouched. Robust thresholding gives the opportunity of a correct separation of the
drawn strokes or text from its background. E ective thresholding very easily separates the text
Effective
written on manuscripts from its background. This paper presents an e ective thresholding method
effective
for binarization of severely degraded and very low appearing gray scale manuscript images. The
proposed method was tested with complex background images of old Indian manuscripts. The
od
method developed in this paper is to recover the textual information as much as possible. Literature
presents implementation of several algorithms for thresholding on various types of document images.

a

b

c

d

f
e
Figure 1: Thresholding results:a) original manuscript image, b) histogram, c) Sauvola method,
d) Otsu method, e) Niblack method, f) Proposed method
289


V.

CONCLUSIONS

This paper described an algorithm that employs adaptive thresholding values to operate over
the manuscript images. The purpose of this work on folio images is to threshold ancient manuscript
images establishing an innovative method. Adjusting the threshold value according to the state of the
image becomes reasonably in selecting gray scale values. It takes into account the improvement in
the image quality as a whole and the increased readability of the texts. Results show that the
proposed method performs better than other thresholding methods. Also, it is robust for document
images in differences based on connectivity and background separation. Thus, no algorithm works
better for all types of images but some work well than others for particular types of images. Hence, it
suggests that for achieving improved performance, selection or combination of appropriate algorithm
for the type of document image under investigation is necessary. The proposed method described a
procedure that utilizes gray scale values of the pixels and image contrast. Many methods require
intensive preprocessing steps to get proper data for working because document image segmentation
techniques are still in infancy. The results show improved image quality of the manuscript images
used in this work. However, this improvement is susceptible to noise, making the method unsuitable
for heavy stained documents.
ACKNOWLEDGEMENT
The author wishes to thank Dr. Anjali Kade, Librarian, University of Mumbai, Mumbai for
providing and allowing to take photographs of Original manuscripts folios used in this work.
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