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An Implementation of Skeletonized Morphological
Factor of Applying Histogram for Text Extraction 1K.R. Sanjuna and
2K. Dinakaran
1Department of Computer Science and Engineering,
Prist University, Tamil Nadu, India.
[email protected] 2Department of Computer Science and Engineering,
RMD Engineering College, Chennai, Tamil Nadu.
Abstract Implementing morphological factor for text processing is a crucial issue
to recognize a text detections. The region selection model has been
effectively developed for image textsegmentation. The accuracy of
detecting system mainly depends on the text preprocessing and
segmentation algorithm being used in this paper, to propose a new method
to segment text regions from color images with textured skeletonized
morphological Factor Algorithm (SMF). This technique is based on
discovery the text edges using materialcontented of the sub image
constants of the discrete wavelet transformed input images. Then, the
noticed edges are mutual to form the exact position of the characters. In the
final stage, the regions that are not satisfactory as the text regions are
detached with segmentation to improve the overall performance and
extract the outlier of text region. The experimental results show that the
projected method is intent to fix line factor text features against size, font,
language, color and direction fluctuations of the text regions.
Key Words:Text extraction, text segmentation, wavelet transform, image
documents.
International Journal of Pure and Applied MathematicsVolume 114 No. 7 2017, 727-741ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu
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1. Introduction
In recent years, text detection has attracted a lot of attention and many text
detection methods have been proposed. Text segmentation refers to the process
of segmenting an article into its several parts based on its content. Because in
the information retrieval systems, a long text tends to be retrieved most
frequently by overestimation of its relevancy to a query, we need to segment it
into its several parts, in order to avoid the problem. In this task, the text is given
as the input and segmented into paragraphs, a list of pairs of adjacent
paragraphs is generated, and each pair is judged whether we put the topic
boundary between them, or not. The task is interpreted into a binary
classification where each pair of paragraphs is classified into separation or non-
separation. However, in next research, it will be considered to segment speech
text into paragraphs or sentences.
Some problems are caused by encoding texts into numerical vectors and
computing their similarities based on only attribute values. Many features are
required for encoding texts into numerical vectors, assuming that words are
given as features, in order to maintain the enough system robustness. The
dominance of zero feature values in each numerical vector causes the very poor
environment for computing their similarities because of very weak
discriminations among numerical vectors. In the previous works, the similarity
between numerical vectors representing texts has been computed, assuming the
independence among features, even if the words which indicate the features
have their very strong semantic relations. Therefore, in this research, as the
challenge against the problems, we consider both the semantic relations among
features and differences among feature values for computing the similarity
between two texts.
Let us mention what we propose in this research as some agenda. In this
research, we assume that words are given as features of numerical vectors in
encoding texts, and they have their semantic relations with others. Based on the
assumption, we define the similarity measure for computing the similarity
between feature vectors, considering both feature values and features. We
modify the KNN into the version where both the feature similarity and the
feature value similarity are used, and apply it to the classification task mapped
from the text segmentation. As benefits from this research, we expect its more
tolerance to the sparse distributions and the potential avoidance of the huge
dimensionality. Let us mention what is expected from this research as benefits
by implementing the above ideas. We may cut down the dimensionality in
encoding texts into numerical vectors, potentially. The information loss in
computing the similarity between texts may be reduced by reflecting the
similarities among the features.
Applying machine learning methods for text detection chance meeting the
difficulties due to atmosphere formation of object recognition. To overwhelmed
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these problems, the implementation begins suggest a two-step localization
verification of morphological approach. The first step purposes at quickly
focusing candidate text lines, permitting the normalization of characters into a
exceptional unit size. In the verification step, a skilled support vector machine
or multi-layer perceptron’s is applied on background independent features to
remove the false alarms. Text recognition, even from the observed text lines,
leftovers a challenging problematic due to the diversity of fonts, colors, the
attendance of complex backgrounds and the short length of the text strings. Two
structures skeletonized factors are examined addressing the text credit problem:
bi-modal improvement scheme and multi-modal subdivision scheme. In the bi-
modal scheme, By text factors object selection is a set of filters to improve the
contrast of black and white characters and crop a better binarization before
recognition. For more over-all cases, the text recognition is lectured by a text
detection formalize the dissection step followed by a traditional optical
character recognition (OCR) algorithm within a multi-hypotheses outline. In the
segmentation step, segmentation model originates the distribution of grayscale
standards of pixels using a Gaussian mixture model or a Markov Random Field.
The subsequent multiple segmentation hypotheses are post-processed by a
associated component object exploration and a grayscale consistency constraint
forms region bounds of text. The proposed approach becomes less sensitive to
the sparse distribution of numerical vectors, because the similarity among
features is captured as well as among feature values. Therefore, we expect both
the better performance of the classification task which is mapped from the text
segmentation and the more efficient text representations, from this research.
2. Literature Survey
In recent years, many methods for text detection have been presented, that prove
effective for text detection in various configurations. They presented a review of
the research on various text detection methods as follows.
The edge-based methods are usually efficient and simple when the edges of text
and background vary considerably. Own to the property of the above
mentioned, edge based methods attracted much attention in these years and
some effective methods have already been developed in many literatures. Sun et
al. [1] used color image filtering technique to extract board text under natural
scenes V. Khare, P. A new histogram oriented moment’s descriptor for multi-
oriented moving text detection Shiva kumara. [2] Located edge-dense image
blocks using edge feature and morphology operation, and then a SVM classifier
was employed to identify the texts blocks. Developed an effective edge-based
text extraction method which was implemented by investigating the location of
text in complex background images.
Y. Zhang, J. H. Lai, and P. C. Yuen, “Text string detection for loosely
constructed characters with arbitrary orientations, [3] developed a method to
detect text, which firstly group text candidates using clustering algorithm, and
identified texts with a text classifier. Edge-based methods can achieve a good
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performance when scene images exhibiting strong edges.
Shuping Liu, Yantuan Xian Text Detection in Natural Scene Images Using
Morphological Component Analysis. The most popular method for text
detection in recent years issparse representation(SR), which inspired by the
sparse-coding mechanism of human vision system. SR technologyhas been
successfully used for face recognition [4], image classification image restoration
and compressed sensing
Xu-Cheng rule, Xuwang Yin, Kaizhu Huang, ANdHongWeiHao [5] projected
at an correct and sturdy technique for detection texts in natural scene photos.
Throughout this paper propose a robust and proper Maximally Stable Extremal
Regions MSER-based scene text detection technique. First, a designed a fast
and effective pruning algorithm may well be a Maximally Stable Extremal
Regions (MSERs); the amount of character candidates to be processed is
reduced with high accuracy. Second, Character candidates are classified into
text candidates by the single-link clustering algorithm, where distance weights
and clustering threshold are learned automatically by a completely unique
(novel) self-training distance metric learning algorithm.
Chucai Yi and YingliTian [6] given a method combines scene text recognition
and scene text detection algorithms. In text detection, projected a Layout based
primarily scene text detection algorithms are applied to get text regions from
scene image. In scene text recognition schemes, structure based scene text
recognition technique is used
Scene Text Recognition applying Structure-Guided Character Detection and
Linguistic Knowledge [7] projected by Cun-Zhao Shi, Chun-Heng Wang, Bai-
Hua Xiao, and Song GAO, Jin-Long Hu projected a completely unique scene
text-recognition technique combination of structure-guided character detection
and linguistic info. Use of every global structure and native look information of
characters, build a part-based tree structure to model each category of characters
so on along observe and acknowledge characters at identical time.
For word recognition, mix the detection scores and language model into the
posterior likelihood of character sequence from the Bayesian decision tree and
the final word recognition result's obtained by finding most likelihood character
sequence utilized by Viterbi algorithm and thus the various information a bit
like the language model to eliminate the word recognition probable ambiguities
A recent paper, Semiautomatic Ground Truth Generation for Text Detection and
Recognition in Video frames [8] proposed by TrungLi, Chew. They projected a
semiautomatic system for ground truth generation for video text detection and
recognition that has English and Chinese text of multi orientation at word level.
Ground trothing for text detection and recognition involves text line
segmentation, word segmentation, bounding box drawing, deciding field,
graphics and scene text separation. The system includes a facility to allow the
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user to manually correct the bottom truth if the machine-driven technique
produces incorrect results
Jacqueline Field [9] projected a bit, a system that handles many stages of scene
text reading in probabilistic manner, from binarization to seem standardization
to character segmentation and recognition. Throughout this work, describe a
reading system that integrates a simple region grouping rule and probabilistic
models for binarizing a given text region, distinctive baselines, along perform
word and character segmentation throughout recognition technique.
In this work projected by Ali Mosleh,NizarBouguila, Abdesamad mount Hamza
[10] projected a bit to erase the unwanted text from the video. Throughout this
work presents a two stages (i) automatic video text detection and (ii) restoration
once the removal. Support Vector Machine (SVM) base video text detection
technique is used to localize the text from video frames. Develop one frame text
detection algorithm using a Stroke Width Transform (SWT) and unsupervised
classification.
Palaiahnakote Shivakumara, TrungQuyPhan,Shijian Lu, and Chew Lim Tan
[11] presents a replacement technique supported gradient vector flow (GVF)
and neighbor part grouping that extracts text lines of any orientations. GVF for
characteristic text component applying Sobel edge map attributable to sobel
provides fine details for text and fewer details for nontext on top of the canny
edge map.
Yao Li, WenjingJia, ChunhuaShen, and Anton van den Hengel [12] proposed
the detection methods to measures of abjectness. Throughout this review
describes the characterless model, regions are extracted by modified MSER-
based region detector Then computed novel characterless cues, then these cues
unit of measurement utilized during a Bayesian framework where naïve Bayes
is used to model the probability
Z. Yuan, D. Zhao, T. Lu, and C. L. Tan “New gradient-spatial-structural
features for video script identification [13]. The input for script identification is
the text blocks obtained by our text frame classification method. A method
based on histogram thresholding, entropy filtering and connected components is
used to extract Bengali text and Bengali characters from multimedia images is
proposed in [15]. A method for character extraction and recognition from
images is proposed in [14], in which edge compactness is designed for four
orientations to notice potential text regions and clustering is used to confine text
regions. A collective approach founded on color and edge landscapes for
extracting text from video is proposed in [16, 17], in which color-edge method
is used to eliminate text contextual and vertical and flatforecast is employed to
locate text in image. The relative education of edge-based and connected-
components based methods in terms of correctness, precision and recall rates is
given in [18], in which each approach is analyzed to determine its success and
limitations.
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3. Problem Definition and Identification
Factor to be Resolved
Text Detection and extraction of region from images includes some stimulating
difficulties. For occurrence, the fonts areoften varied with other substances, the
appeals of region carries may be of any scripts of alphabets with any hue state,
the contextual color may varyonly slightly from that of the fonts, the font style
and size of the charms may vary, and the luminance ofthe imageries may also
vary.
1. To design a hierarchical (i.e., tree-based) representation of the image
contents, where adjacency between components is relatedto inclusion
where it’s give as efficiency
2. To design a character segmentation method which is a good tradeoff
between efficiency (linear time complexity) and quality(with a competitive
F-score);
3. Enhance an efficient grouping of characters into text boxes by
skeletonization method, taking fully advantage of the tree structure
construct to left right position.
4. An illustration on image binarization have the capabilities of the proposed
tree-basedrepresentation.
4. An Implementation of Skeletonized Morphological Factor to Applying
Histogram for Text Extraction
The development of image computational method for extracting character
portions from a complicated image is segmented to outlier portion.
Identification of areas corresponding to text in document images is an important
step for a character recognition system. We briefly review a technique for
automatic design of Skeletonization morphological extraction and show its
application to the segmentation of text areas using region selection from images.
We also present a heuristic applied Gabor filter used to refine the segmentation
results. The goal of the proposed method is to realize a practical document
structure analysis for advanced optical character recognition systems with
Heuristic Feature extraction Model. The color evaluation are carried through
hue-saturation of histogram evaluationThe main objective of this work has been
to develop a robust and efficient segmentation system for natural images.
Architecture Diagram for Proposed Implementation
The figure given below shows the segmentation of morphological factor the
skeletonize the text factor by various processing steps
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Figure 1: Proposed Skeletonization of Morphological Process for Text Extraction
Morphological factor can be achieved by advancing the state-of-art in terms of
pushing forward the edge recognition methods to meet the challenges of the
segmentation task in different situations under extraction processing methods as
shown in figure 1. Consequently, more efficient methods and novel strategies to
issues for which current approaches of previous implementations are developed.
The performance of the presented segmentation produced high performance by
processing this implementation
Pre-processing
Our method is a connected component-based method. Therefore, pre-processing
is an important step to binarize images, extract connected components and
remove noise. First of all, image binarization method is applied to binarize the
image and then all connected components are extracted by he connected
component labeling method. However, not all connected components are
suitable for learning phase or land testing phase such as noise, stains resulting
from the scanning process, etc. Based on the characteristics of connected
components such as size, shape and position, we apply some rules to remove
such small noisy components and stain connected components which usually
have long shapes and appear in the boundary of a document. By sustaining the
Apply Gabor filters Region selection
Input image
Preprocessing Segmentation
A morphological process of applying Histogram Gradient text extraction and classification
Edge mapping
HE feature extraction Technique
Text extraction
Skeletonization
Noise removal
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noise by applying, we equate this,
F( x;m;s)
( )
Where x is variable; m and s, are the mean and standard deviation of the
variable’s natural logarithm for all connected components of the images
respectively
Skeletonized Feature Extraction
Many features can be extracted from connected components. However, if a
selected feature is not good, it does not benefit classification. As shape and
context are very important features with which humans recognize or segment
and image, we extract features from size information, shape information, stroke
width, and position of connected components.
a) Elongation Frame Edge Detection of Skeletonized Text Region
The extraction Frame edges are the dissimilar characteristics of the text blocks
which can be used to notice possible text regions. Here, by discovery the edges
in the revealed sub images and combining the edges controlled in each sub
image, the applicant text regions can be originate. As a result, we initially need
to employ a morphological form edge detector. Here, for computational
efficiency, the Sobel edge detector is used. The Sobel edge detector is efficient
to excerpt the strong edges that are desirable in thisapplication carried forming
edges are outlined. We apply the Sobel edge detector on eachsub image.The
algorithm for computing the edge image E, as follows:
Algorithm 4.1 Input: Text Image Output: Detected Edges
Step 1: Accept the input image for preprocess
Step 2: masking Gx, Gy to the input image
Step 3: Sobel edge detection procedure is applied and the gradient Step
Step 4: Masks manipulation of Gx,Gy separately on the input image
For all the pixels in the gray image G(x,y) do
Calculate left = (G(x,y) – G(x-1,y))
Calculate upper = (G(x,y) – G(x,y-1))
For each (Edge region mapping)
Calculate |G|=√
End
Calculateupper Right = (G(x,y)-G(x+1,y-1))
Calculate. E(x,y) = max( left, upper, upper Right )
End For
Step 5.Sharpen the image E by convolving it with a sharpening filter.
W (x,y) = max( L,U,UR)
Step 6: the absolute magnitude is the output edges
b) Stroke filtering of non-text area region
In this stage, filters are further applied to remove the non-text areas using
strokes structural physical rules. To do so, edge region stimulate summarize the
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shared attributes of horizontal texts as:
a) Edges to form carried texts,
b) Text bars are formed with widths are larger than their heights,
c) Bounded sizes are round to region of object texts and
d) Textsconsume a singular texture property
We generate horizontal and vertical run-length histograms, and from each
histogram we extract the mode, mean, variance and maximal run-length.
Distance to estimated text lines: the distance of an element € to the text lines (L)
is defined in following equation
Wherevi and vj are pixels in e and `, respectively
D (E, L) = max ->vi∈e {min->vj∈ |vi –vj |2}
The lower the value, the more confidence we require from the normal
distribution in order to assign a text label for an element
Segmentation
The Segmentation of the image is carried through candidate extraction model.
Lining edges are outsourced to split the edges using segmentation algorithm,
inwhich text is projected object linear extraction into Lines and Words. They
using the outmodedof structure vertical and horizontal projectionSegmentation
of appealsis faster than the conservative method in which all the letterings from
the text are segmented by associatedcomponent dispensationof extraction only.
Experimental results it ispragmatic that 98% line, word segmentation. The
projected technique starts by segmenting the appearances of line edges and then
words from the binarized de-skewed text image using straight formatted line
joining edge mapping and vertical projection profiles separately splinted. In the
projection skeleton methods, theflat and perpendicular profiles are computed.
To separate text lines, the horizontal projection profile of the text document
imageis found. The horizontal projection profile (HPP) is a Histogram of a
quantity of ON pixels along each row ofthe image. When the projection profiles
are planned, wecan see mountains and valleys in the plot the segmentations are
splitted. White spaceamong the text lines is cast-off to segment the text lines.
Word Segmentation
The line spacing between the wordsis used for word segmentation to restructure
the text formation. Forming text script, spacing among the words is superiorto
thespacing between the characters in a word that easily extract the gap foaming.
The line spacing between the words originates the text joining is found by
captivating the Vertical Projection Profile (VPP) of an input text line.
Removing False Location
In this steps false locations are removed due to the general rules of the text un
text object regions. False locations are un text regions or background of region
outsourced are that is greater than no segment lines are evacuated; this rule will
result in eliminating most of the square images text boxing pixel regions are
that might be found incorrectly as the text regions. This rule results originates
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the exact layer projection of removing singular letters and numbers. In case that
in a particular joining texts are considered as single characters, then this ground
position substitutes by the subsequent.
Algorithm:4.2 Skeletonized Segmentation falseposition Pr, ground pixel labels L
Input: projection of Image
Output: sect ionized image
For(image boundary)
Step 1:segment False point ← un text false (PR).
For each (PR)
Loop: Calculate false location
For end
Step 2: segment False Pixel probabilities
M ← Number of Unique projection images Labels (L)
n1, n2. . . nm ← Counts O uniquelabels(L) .
Get counts of each label on a ground truth image
Step 3: segment Negative pixel probabilities
M←∑ (
.Weighted loss calculation
Get count (loss, 1 n1, 1 n2. . . 1 nm ) . Losspixel with normalization factors
End
Founded square text regions are recognized as single characters without false
crossing regions if their height (width) has a strong-minded size. This text
orientation size is obviously input-dependent. For horizontally regions are
associated in layered text regions, the text block height is amongst two
threshold values. This rule is castoff to eliminate the vertical lines that might
have been noticed in the vertical projection duplicate of the wavelet transform.
These are calculated by probabilities caries the weightage analysis whether the
masking region as are maintained by pixel position.
5. Result and Discussion
The results are implemented in MATLAB with image processing simulation
environment. The projected morphological skeletonized algorithm is tested with
numerous images with trained text regions. We considered only good quality
preprocessed images where there are no overlying to outline suppressed images,
space lining texts, or broken characters. The proposed Skeletonized
morphological factor SMF has produced efficient results than another classifier.
We have evaluated the proposed algorithm with different methodologies
discussed earlier.
The resultant screen shot given below shows that text recognition by
skeletonized morphological applied at in skew factors
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Figure 3: Initaia Skew Process Figure 2: Iteration of Morphological Factors
Figure 2 shows the initial process of morphological factor preprocessing state of
MSE, PSNR, and RMSE evaluation. Figure 3 represent the iteration of
morpholical occurrence skeletonized process state.
Figure 4: Mean Square Estimation Figure 5: Number of Text Regions
Figure 4 shows the estimation of feature level by mean square error with region
growing levels .figure 5 shows the observation of text from text region
Graph 1: Shows the Text segmentation Accuracy Achieved by Different
Methods
Graph 1 shows text detection in natural scene images is challenging for complex
background. There are many methods available for detecting the text and
recognition from natural scene images. Here we present a new SMF based
technique has produced higher performance.
0
50
100
150
2 4 6 8 10Text
se
gme
nta
tio
n in
%
Time (min)
Conditional random field
Discrete Wavelet Transform
MSER-DFE
SMF
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Graph 2: Graphical Representation of Performance Ratio
In Graph 2, the performance of SMF is shown, which is compared with other
methods based on accuracy. The SMF is having higher performance rate
compared to than other techniques.
Graph 3: Graphical Representation of Processing Time Performance
In graph 3 the processing of time performance has been shown below. In SMF
with neural network method, the classification would be done in 4sec and the
accuracy increases up to more level.
6. Conclusion
For future work, we are considering advanced preprocessing to resolve
problems in distinguishing non-text incontemporary images. Moreover, we will
investigate theuse of automatic feature learning for text versus non-text
discrimination. This paper has presented a Skeletonization method for
segmenting the text and non-text in document images. The method is based on a
set of powerful connected component features. Those features utilize size,
shape, and stroke width and position informationof connected components.
Morphological trained on those features to obtain a model for labelling
connected components. Our results show that the method issimple, fast and is
really able to discriminate text from context, including the text that appears
within graphical.
0
20
40
60
80
100
SVM MRE Linear DWT MSER-DFE SMFTex
t se
gm
enta
tion
in
%
Various method
0
5
10
15
20
30 60 90Tim
e t
ake
n in
m/s
various iteration
SVM
MRE
DWT
MSER-DFE
SMF
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