Intelligent Information Management, 2010, 2, 120-133
doi:10.4236/iim.2010.22015 Published Online February 2010 (
Copyright © 2010 SciRes IIM
Text Extraction in Complex Color Document
Images for Enhanced Readability
P. Nagabhushan, S. Nirmala
Department of Studies in Computer Science, University of Mysore, Mysore, India
Email:, nir_shiv_200
Often we encounter documents with text printed on complex color background. Readability of textual con-
tents in such documents is very poor due to complexity of the background and mix up of color(s) of fore-
ground text with colors of background. Automatic segmentation of foreground text in such document images
is very much essential for smooth reading of the document contents either by human or by machine. In this
paper we propose a novel approach to extract the foreground text in color document images having complex
background. The proposed approach is a hybrid approach which combines connected component and texture
feature analysis of potential text regions. The proposed approach utilizes Canny edge detector to detect all
possible text edge pixels. Connected component analysis is performed on these edge pixels to identify can-
didate text regions. Because of background complexity it is also possible that a non-text region may be iden-
tified as a text region. This problem is overcome by analyzing the texture features of potential text region
corresponding to each connected component. An unsupervised local thresholding is devised to perform fore-
ground segmentation in detected text regions. Finally the text regions which are noisy are identified and re-
processed to further enhance the quality of retrieved foreground. The proposed approach can handle docu-
ment images with varying background of multiple colors and texture; and foreground text in any color, font,
size and orientation. Experimental results show that the proposed algorithm detects on an average 97.12% of
text regions in the source document. Readability of the extracted foreground text is illustrated through Opti-
cal character recognition (OCR) in case the text is in English. The proposed approach is compared with some
existing methods of foreground separation in document images. Experimental results show that our approach
performs better.
Keywords: Color Document Image, Complex Background, Connected Component Analysis, Segmentation
of Text, Texture Analysis, Unsupervised Thresholding, OCR
1. Introduction
Most of the information available today is either on pa-
per or in the form of still photographs, videos and elec-
tronic medium. Rapid development of multimedia tech-
nology in real life has resulted in the enhancement of the
background decoration as an attempt to make the docu-
ments more colorful and attractive. Presence of uniform
or non-uniform background patterns, presence of multi-
ple colors in the background, mix up of foreground text
color with background color in documents make the
documents more attractive but deteriorates the readability.
Some of the examples are advertisements, news paper
articles, decorative postal envelopes, magazine pages,
decorative letter pads, grade sheets and story books of
children. Further, the background patterns opted in the
preparation of power point slides appear to be attractive
but cause difficulty in reading the contents during pres-
entation on the screen. These compel to devise methods
to reduce the adverse effect of background on the fore-
ground without losing information in the foreground.
There are many applications in document engineering
in which automatic detection and extraction of fore-
ground text from complex background is useful. These
applications include building of name card database by
extracting name card information from fanciful name
cards, automatic mail sorting by extracting the mail ad-
dress information from decorative postal envelopes [1].
If the text is printed on a clean background then certainly
OCR can detect the text regions and convert the text into
ASCII form [2]. Several commercially available OCR
products perform this; however they result in low recog-
nition accuracy when the text is printed against shaded
and/or complex background.
The problem of segmentation of text information from
complex background in document images is difficult and
still remains a challenging problem. Development of a
generic strategy or an algorithm for isolation of fore-
ground text in such document images is difficult because
of high level of variability and complexity of the back-
ground. In the past, many efforts were reported on the
foreground segmentation in document images [3–16].
Thresholding is the simplest method among all the
methods reported on extraction of foreground objects
from the background in images. Sezgin and Sankur [14]
carried out an exhaustive survey of image thresholding
methods. They categorized the thresholding methods
according to the information they are exploiting, such as
histogram shape based methods, clustering based meth-
ods, entropy based methods, object-attributes based
methods, spatial methods and local methods. The choice
of a proper algorithm is mainly based on the type of im-
ages to be analyzed. Global thresholding [7,11] tech-
niques extract objects from images having uniform
background. Such methods are simple and fast but they
cannot be adapted in case the background is non uniform
and complex. Local thresholding methods are window
based and compute different threshold values to different
regions in the image [8,14] using local image statistics.
The local adaptive thresholding approaches are also
window based and compute threshold for each pixel us-
ing local neighborhood information [9,13]. Trier and Jain
[17] evaluated 11 popular local thresholding methods on
scanned documents and reported that Niblack’s method
[9] performs best for OCR. The evaluation of local
methods in [17] is in the context of digit recognition.
Sauvola and Pietikainen [13] proposed an improved ver-
sion of Niblack method especially for stained and badly
illuminated document images. The approaches proposed
in [9,13] are based on the hypothesis that the gray values
of text are close to 0 (black) and background pixels are
close to 255 (white). Leedham et al. [8] evaluated the
performance of five popular local thresholding methods
on four types of “difficult” document images where con-
siderable background noise or variation in contrast and
illumination exists. They reported that no single algo-
rithm works well for all types of image. Another draw-
back of local thresholding approaches is that the proc-
essing cost is high. Still there is a scope to reduce the
processing cost and improve the results of segmentation
of foreground text from background by capturing and
thresholding the regions containing text information.
Often we encounter the documents with font of any color,
size and orientation. Figure 1 shows some sample color
document images where the foreground text varies in
color, size and orientation. Conventional binarization
methods assume that the polarities of the foreground and
background intensity are known apriori; but practically it
is not possible to know foreground and background color
intensity in advance. This drawback of conventional
thresholding methods call for specialized binarization.
Text-regions in a document image can be detected ei-
ther by connected component analysis [3,18] or by tex-
ture analysis method [1,19]. The connected component
based methods detect the text based on the analysis of
( a ) ( b )
( c ) ( d )
Figure 1. Color documents with printed text of different
size, color and orientation.
the geometrical arrangement of the edges [16] that com-
pose the characters. They are simple to implement and
detect text at faster rate but are not very robust for text
localization and also result in false text regions for im-
ages having complex background. Pietik¨ainen and Okun
[12] used edge detectors to extract the text from docu-
ment images. Their method fails in extracting the tilted
text lines and erroneously classifies the textured back-
ground as text. Chen et al. [3] proposed a method to de-
tect the vertical and horizontal edges in an image. They
used different dilation operators for these two kinds of
edges. Real text regions are then identified using support
vector machine. The method lacks in detecting text tilted
in any orientation. Zhong et al. [18] used edge informa-
tion to detect the text lines. Their method deals with
complex color images pretty well but restricted to certain
size constraints on characters. The texture based methods
detect the text regions based on the fact that text and
background have different textures [1]. In [19] it is as-
sumed that text is aligned horizontally or vertically and
text font size is in limited range. The method proposed in
[19] uses texture features to extract text but fails in case
Copyright © 2010 SciRes IIM
of small font size characters. Their method is based on
the assumption that the text direction is horizontal or
vertical. In [2] texture based method is proposed to de-
tect text regions in gray scale documents having textured
background. In their method text strokes are extracted
from the detected text regions using some heuristics on
text strings such as height, spacing, and alignment [2].
The extracted text strokes are enclosed in rectangular
boxes and then binarized to separate the text from the
background. Their method fails to extract the text in low
contrast document images. Also they fail to extract the
tilted text in document images. Most of the above meth-
ods are very restrictive in alignment and type of the text
they can process. Sobotta et al. [15] proposed a method
that uses color information to extract the text in colored
books and journal covers. Their method fails to extract
the isolated characters. In [4] a method is proposed to
separate foreground from background in low quality an-
cient document images. The test documents used in their
method are scanner based handwritten, printed manu-
scripts of popular writers. Their method fails to segment
the foreground text in documents with textured back-
ground. Liu et al. [20] proposed a hybrid approach to
detect and verify the text regions and then binarize the
text regions using expectation maximization algorithm.
The computation complexity of verification process of
the text region is high. The performance of the algorithm
proposed in [20] degrades when the documents have
high complex background and fails to extract the text in
low contrast document images. Kasar et al. [6] proposed
a specialized binarization to separate the characters from
the background. They addressed the degradations in-
duced in camera based document images such as uneven
lighting and blur. The approach fails to extract the text in
document images having textured background. It also
fails to detect the characters in low resolution document
images. From the literature survey, it is evident that iden-
tifying, separating the foreground text in document im-
ages and making it smoothly readable is still a research
issue in case the background of a document is highly
complex and the text in foreground takes any color, font,
size and tilt.
In this paper we propose a novel hybrid approach to
extract the foreground text from complex background.
The proposed approach is a five stage method. In the first
stage the candidate text regions are identified based on
edge detection followed by connected component analy-
sis. Because of background complexity the non-text re-
gion may also be detected as text region. In the second
stage the false text regions are reduced by extracting the
texture feature and analyzing the feature value of candi-
date text regions. In the third stage we separate the text
from the background in the image segments narrowed
down to contain text using a specialized binarization
technique which is unsupervised. In the fourth stage the
text segments that would still contain noise are identified.
In final stage the noise affected regions are reprocessed
to further improve the readability of the retrieved fore-
ground text. The rest of the paper is organized as follows.
Section 2 introduces our approach. In Section 3 experi-
mental results and discussion are provided. Time com-
plexity analysis is provided in Section 4. Conclusions
drawn from this study are summarized in Section 5.
2. Proposed Approach
In this work we have addressed the problem of improv-
ing the readability of foreground text in text dominant
color document images having complex background by
separating the foreground from the background. The
proposed work is based on the assumption that the fore-
ground text is printed text. Two special characteristics
of the printed text are used to detect the candidate text
regions. They are, 1) Printed characters exhibit regular-
ity in separation and 2) Due to high intensity gradient, a
character always forms edges against its background.
The sequence of the stages in proposed hybrid approach
is shown in Figure 2. The proposed five stage approach
is described in the subsections to follow.
2.1. Detection of Text Regions
The proposed method uses Canny edge detector to de-
tect edges [21] because Canny edge operator has two
advantages: it has low probability of missing an edge
and at the same time it has some resistance to the pres-
ence of noise. We conducted experiments on both gray
scale and RGB color model of source document images.
It is observed from the experimental evaluations that the
edge detection in gray scale document images resulted
in loss of text edge pixels to certain extent. Hence edge
detection in RGB color model of source document is
proposed instead of transforming the color document to
Input color
document image
Detection of candidate text
Removal of false text regions
Thresholding of text regions
Detection of noisy text re-
Reprocessing of noisy text re-
foreground text
Figure 2. Stages of the proposed approach.
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Figure 3. Holes in a connected component.
( a ) ( b )
Figure 4. (a) Word that composes characters without holes, (b) holes created by connecting the characters in the word.
a gray scale document. Edge detection is carried out in
each color channel separately as the foreground text may
be of any color and therefore the edges could be visible
in one or more of these three color channels. The results
of the edge detection of all the three color channels are
assimilated so that no character edge gets missed. Sup-
pose E R, E
G and EB are the images after applying the
Canny edge operator on red, green and blue components
of the input color image, the resulting edge image “E”
after assimilation is given by,
E=E R V EG V E B (1)
where “V” represents logical “OR” operator.
The resulting edge image “E” contains edges corre-
sponding to character objects in the input image. When
the background is highly complex and decorative, the
edge image “E” might contain edges corresponding to
non-text objects also. An 8-connected component label-
ing follows the edge detection step. The non-text com-
ponents in the background such as underlines, border
lines, and single lines without touching foreground char-
acters do not contain any hole. A hole in a connected
component is illustrated in Figure 3.
Generally in a document image some printed charac-
ters contain one or more holes and some other characters
do not contain a hole. If a word is composed of charac-
ters without holes, using dilation operation [3] it could be
possible to thicken the characters so that they get con-
nected and additional holes are created in the space be-
tween the characters. This process is depicted in Figure4.
As the text lines in most of the documents are conven-
tionally aligned horizontally, we conducted experiments
on dilation of the edge image “E” in horizontal direction.
It is observed from experimental evaluations that dilation
of edge image “E”, only in horizontal direction is not
enough to create holes in most of the connected compo-
nents corresponding to character strings. Therefore we
extended the dilation operation on the edge image in both
horizontal and vertical directions. From the experimental
results it is observed that dilation of the edge image in
both horizontal and vertical directions has created holes
in most of the connected components that corresponds to
character strings. The size of the structuring element for
dilation operation was fixed based on experimental
evaluation. As no standard corpus of document images is
available for this work we conducted experiments on the
document images collected and synthesized by us which
depict varying background of multiple colors and fore-
ground text in any color, font, size. We dilated the edge
image row-wise and column-wise with line structuring
element of different sizes. Table 1(a) and Table 1(b)
show the percentage loss of characters in a document
image after dilating the edge image “E” with various
sizes of horizontal structuring element and vertical
structuring element.
Table 1(a). Percentage loss of characters for various sizes of
horizontal structuring element.
Size of vertical structuring element is 3X1,
total number of characters processed=6171
Size of the
horizontal structur-
ing element
1x2 1x3 1x4 1x5 1x6
Loss of
characters in per-
1.931.93 2.37 2.384.58
Table 1(b). Percentage loss of characters for various sizes of
vertical structuring element.
Size of horizontal structuring element is 1x3,
total number of characters processed=6171
Size of the verti-
cal structuring
2x1 3x1 4x1 5x1 6x1
Loss of charac-
ters in percentage 1.99 1.96 1.98 1.98 4.18
From Table 1(a) and Table 1(b) it is observed that with
horizontal structuring element of size 1x3 and vertical
structuring element of size 3x1, the percentage loss of
characters in a document image is very low. This indi-
cates that the dilation of edge image with line structuring
element 1x3 in horizontal direction and line structuring
element 3x1 in vertical direction creates additional holes
in most of the text components which is depicted in Fig-
ure 4. Figure 5 shows the document image after assimi-
lating the results of horizontal and vertical dilation of
edge image of the input image which is shown in Figure
The 8-connected component labeling is performed on
the dilated edge image. Based on the size of the charac-
ters in the source document and spacing between the
words the so labeled connected components may be
composed of a single character or an entire word or part
of the word or a line. The labeled component may also
contain words from different lines if the words in differ-
ent lines are connected by some background object. In
this work the built-in function “Bwboundaries” in
MATLAB image processing tool box is used to find the
holes in a connected component. The connected compo-
nents are analyzed to identify the object/component con-
taining hole. We removed the connected components
without hole(s). Other non-text components are elimi-
nated by computing and analyzing the standard deviation
of each connected component which is elaborated in the
next subsection.
Figure 5. Document image after dilation.
2.2. Removal of False Text Regions
Because of background complexity certain amount of
non-text region in the source document might be identi-
fied as text region in connected component analysis
process. The proposed approach is based on the idea that
the connected components that compose textual informa-
tion will always contain holes. Holes in the connected
components comprise the pixels from the background.
Hence each connected component represents an image
segment containing only background pixels in case there
is no text information (false text region) or both fore-
ground and background pixels in case the connected
component contains text information (true text region).
To remove the image segments containing only back-
ground pixels, standard deviation of gray scale values of
all pixels in each image segment/connected component is
calculated. The standard deviation in the image segments
occupied with only background pixels (ie, image seg-
ments without text) is very low where as the standard
deviation in the image segments occupied by both back-
ground and foreground pixels (ie, image segments con-
taining text) is high [10]. Based on this characteristic
property of document image it could be possible to dis-
criminate the non-text image segments from image seg-
ments containing text. To set the value for “SD” we con-
ducted experiments on document images having uni-
form/non-uniform background of multiple colors and
foreground text of any font, color, size and orientation.
We set the value for standard deviation from a set of 120
images (first 120 images in the corpus).The document
image samples are selected randomly in multiples of 5,
from the corpus of images synthesized and collected by
us, to set the empirical value for standard deviation’ SD’.
The sample images selected are all distinct images from
the corpus of images. From the plot shown in Figure 6, it
is observed that a threshold value of 0.4 on “SD” is suf-
ficient enough to filter out the non-text regions without
loss of detected text. In addition repeating the experiment
10 times on 50 distinct samples selected randomly each
time (from first 120 samples in the corpus), demonstrated
that the value for standard deviation falls in the range
0.405 to 0.42. We extended the experiment on 100 more
images in the corpus apart from sample images used for
setting the value for “SD” and observed that SD=0.4
resulted in reduction of the false text regions without loss
of text information in the document. However, although
choosing a higher “SD” value reduces the false text re-
gions it results in the loss of foreground text and choos-
ing “SD” value lower than 0.4 leads to additional proc-
essing of more number of false text regions. Hence stan-
dard deviation of 0.4 is chosen as the threshold value.
2.3. Extraction of Foreground Text
n the proposed approach color information is not used to
xtract the foreground text. As already during the first
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Figure 6. Plot showing the number of training sample images versus the SD value for no loss of textual information.
scale intensity values (gray value near to 0 for fore-
ground pixels and gray value 255 for background pixels)
are assigned to pixels in the output image. Irrespective of
the foreground text color and background color we pro-
duced black characters on uniform white background by
suitably thresholding each image segment containing text
and producing the corresponding output image segment
Obw using the logic as given by,
stage of our approach the evidences of textual edges have
been drawn from intensity values of each color channel
(RGB model). Also it is computationally inexpensive to
threshold the gray scale of the image segment corre-
sponding to the connected component by tightly encap-
sulating the segment. Figure 7 illustrates background and
foreground pixels in a connected component. In each
connected component average gray scale intensity value
of foreground pixels and average gray scale intensity
value of the background pixels are computed.
 thresholdyxIif
OVVif bwbf ),(255
Suppose “m” and “s” are mean and standard deviation
of gray scale intensities in an image segment corre-
sponding to a connected component with hole(s), the
threshold value for that segment is derived automatically
from the image data as given in [9],
 thresholdyxIif
Irrespective of the foreground text color and back-
ground color the extracted characters are produced in
black color on uniform white background for the purpose
of improving the readability of the document contents.
The resulting image might contain noise in the form of
false foreground. This needs reprocessing of the resulting
image to further improve the readability of document
contents by OCR.
threshold=m-k*s (2)
where (k) is a control parameter and value of (k) is de-
cided based on the average gray scale intensity value of
foreground pixels and average gray scale intensity value
of background pixels. Suppose “Vf” is average gray scale
intensity value of foreground pixels and “Vb” is average
gray scale intensity value of background pixels. We con-
ducted experiments on document images with varying
background and foreground text of different colors. From
experimental evaluations it is observed that choosing
k=0.05 for Vf >Vb and k=0.4 for Vf Vb results in a better
threshold value. In this work to discriminate foreground
pixels from background pixels two contrast gray
2.4. Detection of Noisy Text Regions
Detection of text areas/segments that need further proc-
essing is performed using a simple method. The main
idea is based on the fact that the text areas that still con-
tain noise include more black pixels on an average in
comparison to other text areas/segments. The image is
divided into segments of variable sizes; each segment
corresponds to one connected component. In each image
segment that contains text the density of black pixels, f(S)
is computed. Suppose b(S) is frequency of black pixels
in an image segment “S” and area(S) is area of image-
segment “S”, the density of black pixels in “S” is given by,
f(S)= b(S)/area(S) (3)
Figure 7. Illustration of foreground and background pixels
in a connected component. The segments that satisfy the criterion f(S)>c*d, are
selected for reprocessing, where “d” is the average den-
sity of black pixels of all the image segments containing
text. The parameter “c” determines the sensitivity of de-
tecting noisy text regions. High value of “c” results in
less text segments to be reprocessed. Low value of “c”
results in more text segments to be reprocessed which
would include the text segments in which noise is al-
ready removed. Figure 8 shows the noisy areas to be re-
processed for different values of “c”. Optimal value for
parameter “c” is selected based on higher character (or
word) recognition rate after reprocessing noisy text re-
gions in the output document image. We conducted ex-
periments on the document images which we collected
and synthesized. Table 2 shows the character and word
recognition rates in percentage for various values of “c”.
It is observed from Table 2 that character (or word) rec-
ognition rate is high for value of c<=0.5. Also it is seen
from Figure 8 that number of components to be reproc-
essed will be less as the value of “c” increases. So 0.5 is
chosen as optimal value for parameter c.
2.5. Reprocessing of Noisy Text Regions
The selected text segments containing noise in the form
of false foreground pixels are reprocessed. Repeating the
stage-1 on these text segments leads into text segments
of smaller size. These segments are thresholded in the
next stage. Since only few text segments are reprocessed
instead of all the detected and verified text segments, the
computation complexity of the stage-4 reduces substan-
tially. In fact, the entire approach can be proposed to be
iterative if it is required; but we observed that repeating
stage-1 and stage-3 once on noisy regions is more than
sufficient which in turn reduces the time complexity of
extracting the foreground text from complex background
in document images. Figure 9 shows the results at each
stage in the proposed approach.
3. Results and Discussions
3.1. Experimental Results
Since no standard corpus of document images is avail-
able for this work we created a collection of images by
scanning the pages from magazines, story books of chil-
dren, newspapers, decorative postal envelopes and invi-
tation cards. In addition, one more dataset of synthesized
images which are of low resolution is created by us. The
details of documents in the corpus of images used for
testing our proposed algorithm are depicted in Table 3.
The number of document images in our datasets is 220
and they are of different resolutions (96x96 DPI,
100x100 DPI, 150x150 DPI and 200x200 DPI). The
output image is obtained by depositing the black charac-
ters on the white background, irrespective of the back-
ground and foreground color in the original document.
The performance of text region detection is evaluated in
terms of Recall (correct detects/(correct detects + missed
detects)) and Precision (correct detects/(correct detects+
false alarms)). Recall is inversely proportional to missed
detects whereas Precision is inversely proportional to
false alarms. Missed detects indicates number of text
regions incorrectly classified as non text and false alarms
indicates number of non-text regions incorrectly classi-
fied as text regions. Table 4 shows the average value of
precision and recall in percentage for document images
in the corpus.
(a) (b) (c)
Figure 8. Noisy text segments selected based on value of c: (a) c=0.5 (b) c=1.0 (c) c=1.5.
Table 2. Character and word recognition rates for various values of “c”.
c<0.5 c=0.5 c=1.0 c=1.5
Character recognition rate ( % ) 82.93 82.93 81.26 80.98
Word recognition rate ( % ) 71.33 71.33 70.10 67.38
Copyright © 2010 SciRes IIM
Input image (a) (b)
(c) (d) (e)
Figure 9. Result at each stage using proposed algorithm. (a) candidate text regions, (b) verified text regions, (c) extracted
foreground text with noisy areas, (d) detection of noisy text segments (c=0.5), (e) extracted foreground text after reprocessing
noisy text segments.
The proposed approach focuses on documents with
English as text medium because we could quantify the
performance of the improvement in the readability of
document images by employing an OCR. Reading of the
extracted text for documents in English as text medium is
evaluated on Readiris 10.04 pro OCR. Readiris OCR
converts the input document image to binary form before
recognizing the characters. The Readiris 10.04 pro OCR
can tolerate a skew of 0.5 degrees on foreground text.
Readability of the segmented foreground text is evalu-
ated in terms of character and word recognition rates.
OCR results for document images with printed English
text are tabulated in Table 5. From Table 5 it is seen that
average recognition rate at character level is higher
compared to word level.
Observations from the experimental evaluation are as
Copyright © 2010 SciRes IIM
Table 3. Details of document collection used for this work.
Document types Language Background complexity Foreground complexity
1) Pages from Magazines
2) Pages from Story books
of children
3) Postal envelopes
4) Articles from newspapers
5) Power point slides
6) Journal cover pages
7) Invitation cards
Mainly in Eng-
lish. Also in
other languages
1) Uniform patterned back-
2) Non uniform patterned
3) Background designs from
Microsoft power point
4) Single and multicolored
1) Single colored and mul-
ticolored text
2) Text tilted in any orien-
3) Text of varying sizes
4) Foreground with dense
text and sparse text
Table 4. Results showing text region detection.
Documents in English
Documents in Kannada
Documents in Malayalam
Number of samples 180 30 10
Total number of characters 31784 4710 1068
Total number of words 6354 1200 317
Recall ( % ) 97.06 96.26 100
Precision ( % ) 96.78 95.1 90.23
Number of characters (or words) correctly recognized
Character (or word) recognition rate= Total number of characters (or words) in source document image
Table 5. OCR results for English documents.
Average Recognition Rates (%)
Original document Processed document After further processing the noisy
areas in the processed document
Character level 42.99 80.31 82.93
Word level 36.47 67.55 71.33
For some document images the readability by the
OCR without using our approach is 100% and the same
is maintained even after applying our approach. [The
proposed approach has not deteriorated the readability!].
For rest of the documents due to high complexity of
the background the readability through OCR is very low
or even nil. After applying our approach the readability
of document contents by OCR is improved to nearly
From Table 5 it is evident that the word and character
recognition rates are enhanced after applying our ap-
proach. Further, it can be noted that readability is further
improved after reprocessing the noisy areas in the output
document images.
Many times the text lines in a document are tilted
/rotated as an attempt to make the contents of the docu-
ment more attractive. We extended our approach to ex-
tract the foreground text in document images with text
lines tilted in any orientation. From experimental results
it is evident that dilation of the edge image “E” in hori-
zontal and vertical direction is sufficient to identify the
text regions in document images having tilted text lines.
Sample document images with foreground text lines ti-
tled in any orientation and the corresponding results are
shown in Figure 10.
For documents with English as medium of text, we
were able to quantify the enhanced readability through
OCR and for documents in other languages we verified
the extracted foreground text by visual inspection of
output images, which indicates successful segmentation
of foreground text from complex background. Figure 11
shows results of the proposed approach for documents in
Malayalam and Kannada languages.
3.2. Discussions
Results of the proposed approach are compared with
results of some existing methods of foreground separa-
tion in document images [6,9,13]. For a sample text rich
document image and sparse text document image the
output images obtained from the proposed method and
other methods [6,9,13] are shown in Figure 12(a) and
Figure 12(b) respectively.
From visual inspection of the results shown in Figure
12(a) and Figure 12(b) it is observed that, Niblack
method fails to separate the foreground from complex
background. Kasar method resulted in loss of foreground
text information. Even though Sauvola method extracted
foreground text, it introduced lot of noise compared to
proposed method.
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Figure 10. Document images with tilted foreground text: (a) Input color image, (b) Output image.
(a) (b) (c) (d)
Figure 11. Results of the proposed approach: (a,c) Input color images, (b,d) Output images.
Copyright © 2010 SciRes IIM
Input color image
Niblack method Sauvola method
Kasar method Proposed method
Figure 12(a). Comparison of results of foreground text separation from complex background in text rich document image.
Input color image
Niblack method Sauvola method
Kasar method Proposed method
Figure 12(b). Comparison of results of foreground text separation from complex background in postal document image.
Copyright © 2010 SciRes IIM
Copyright © 2010 SciRes IIM
We created 20 ground truth images by selecting com-
plex textures from Microsoft power point designs. On
one set of 10 different backgrounds with varying com-
plexities the same textual content of 540 characters is
superimposed. Similarly on another set of 10 images
created, each is superimposed with the same postal ad-
dress shown in Figure 12(b). The outcome of the ex-
periments on these ground truth images is shown in Ta-
ble 6. As the output image produced by Niblack is too
noisy the amount of characters recognized by OCR is
very less. Kasar method fails to detect the foreground
characters in document images having textured back-
ground which resulted in loss of text information present
in the foreground of the input document image. This
leads to very low character recognition accuracy by OCR.
As the output images produced by Niblack, Sauvola and
Kasar methods are noisier compared to the proposed
method the amount of characters recognized by OCR is
low which is evident from Table 6. These existing meth-
ods do not perform well when documents have tex-
tured/patterned background. This drawback is overcome
by the proposed method. Yet in another experiment, a set
of 10 typical document images from the corpus were
tested with Niblack, Sauvola, Kasar and proposed
method. The readability of extracted foreground text is
evaluated on Readiris pro 10.04 OCR. The number of
characters recognized by OCR is described in Table 7.
Our approach successfully separates the foreground in
document images which are of low resolution and free
from degradations such as blur, uneven lighting, and
wavy patterned text. From Table 7 it is evident that our
approach performs well for complex background color
document images compared to the methods [6,9,13] and
leads to higher character recognition accuracy through
One advantage of proposed method over the existing
conventional approaches is it successfully extracts the
foreground text without a prior knowledge of foreground
and background polarities. Another advantage over ex-
isting methods is it is less expensive as it detects the im-
age segments containing text and extracts the text from
detected text segments without using the color informa-
tion. The approach is independent of medium of fore-
ground text as it works on edge information.
4. Time Complexity Analysis
Suppose size of the document image is MxN. Accord-
ingly the size of RGB color image is 3xMxN. So the
total number of pixels in input color image I is 3xMxN.
The time complexity of the proposed algorithm in order
notation is O(N2) if M=N. For the purpose of profiling
Table 6. Details of Foreground text extraction results on ground truth images by OCR.
Sauvola me-
thod Kasar method Proposed method
Image type Number of
characters CRR (%) CRR (%) CRR (%) CRR (%)
Text rich document 540 27.89 89.63 76.33 98.53
Postal document 50 8.60 58.00 27.00 83.00
CRR–Average Character Recognition Rate when output image is OCRed.
Table 7. OCR based recognition of characters: Details for 10 test images with complex background.
Source of the document
Number of char-
acters NCR (%) NCR (%) NCR (%) NCR (%)
News paper 483 0 13.66 70.39 97.52
News paper 300 20 99.66 94.00 98.33
Magazine 144 0 0 0 92.36
Invitation card 748 0 98.93 95.45 95.32
Story book 300 0 96.66 44.66 98.66
Story book 440 0 99.09 51.59 99.55
Story book 139 0 97.12 73.38 100
Synthesized image 398 0 0 3.52 93.72
Postal doc. 47 0 0 51.06 100
Postal doc. 50 0 0 0 82
Average 305 2 50.51 48.41 95.75
NCR–Number of characters recognized by OCR.
Figure 13. Plot showing execution time of each stage in proposed approach.
we have kept the size of all the test document images
uniform (350x600 pixels). Postal document images con-
tain text in sparse i.e., only printed postal address, on an
average of 45 characters. Text rich document images
contain text in dense, on an average of 200 characters.
The algorithm was executed on a Intel(R) Core(TM) 2
Duo CPU, 2.20GHz, 1GB RAM. It is observed that total
time needed to extract the foreground text is high for text
rich document images compared to sparse text document
images. Time needed to process the document depends
on the amount of text in the foreground of the source
document image. The total time of the entire process
includes the time of I/O operations of document images
also. Figure 13 shows the plot of execution time of each
stage of the proposed approach for document images
with varying density of textual information.
5. Conclusions and Future Work
In this paper a hybrid approach is presented for extrac-
tion of foreground text from complex color document
images. The proposed approach combines connected
component analysis and texture feature analysis to detect
the segments of image containing text. An unsupervised
local thresholding method is used to extract the fore-
ground text in segments of image containing textual in-
formation. A simple and computationally less expensive
method for texture analysis of image segments is pro-
posed for reduction of false text regions. We have not
used color information in extracting the text since in the
first stage of our approach the evidence of all textual
edges comes from intensity values in each color channel
(RGB model) and this makes computations inexpensive.
Threshold value to separate the foreground text is de-
rived from the image data and does not need any manual
tuning. The proposed algorithm detects on an average
97.12% of text regions in source document image. The
shortcomings of the proposed approach are 1) it fails to
separate the foreground text when the contrast between
the foreground and background is very poor 2) it fails to
detect single letter word which neither contains a hole
nor creates a hole by the dilation.
The algorithm has so far been tested on text dominant
documents only which are scanned from news papers,
magazines, story books of children, postal envelopes.
Also we tested the proposed approach on synthesized
images. The behavior of the documents containing
graphic objects in foreground is considered as future
extension of the present work. Design of post processing
steps to recover the missed single character words with-
out holes is another future direction of the current study.
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