Script identification from printed document images using statistical

International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 2, March – April (2013), © IAEME
607
SCRIPT IDENTIFICATION FROM PRINTED DOCUMENT IMAGES
USING STATISTICAL FEATURES
M. M. Kodabagi1
, S. R. Karjol2
1
Department of Computer Science and Engineering,
Basaveshwar Engineering College, Bagalkot-587102, Karnataka, India
2
Department of Computer Science and Engineering,
Basaveshwar Engineering College, Bagalkot-587102, Karnataka, India
ABSTRACT
Automatic identification of a script in a document image facilitates many important
applications such as automatic archiving of multilingual documents; searching online archives of
document images and for the selection of script specific OCR in a multilingual environment. In
this work a technique for script identification from document images is proposed. The method
uses vertical and horizontal run components/objects of words of a single line of text to
distinguish 3 Indian scripts: Kannada, Hindi and English. Initially, the method segments words
from the selected line of text from a document image. Then statistics of horizontal and vertical
run objects are determined. Further, linear discriminant function is used to identify script of the
document image as Kannada, Hindi or English script. The method has been tested for 300
document images and the method found to be robust and efficient. The proposed system achieves
93% identification accuracy for Hindi script, 90% identification accuracy for English script and
86% identification accuracy for Kannada script.
1. INTRODUCTION
In recent years, the escalating use of physical documents has made progress towards the
creation of electronic documents to facilitate easy communication and storage of documents.
However, the usage of physical documents is still prevalent in most of the communications. The
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ISSN 0976 – 6367(Print)
ISSN 0976 – 6375(Online)
Volume 4, Issue 2, March – April (2013), pp. 607-622
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amount of creation and storage of electronic documents is increasing rapidly with the advances
in computer technology. Such data include multi-lingual documents. For example, museums
store images of old fragile documents in typically large databases. These documents have
scientific or historical or artistic value and can be written in different scripts. Document analysis
systems that help process these stored images is of interest for both efficient archival and to
provide access to various researchers. Script identification is a key step that arises in document
image analysis especially when the environment is multiscript and multi-lingual. An automatic
script identification scheme is useful to (i) sort document images, (ii) select appropriate script-
specific OCRs and (iii) search online archives of document images for those containing a
particular script.
India is a multi-script multi-lingual country and hence most of the document including
official ones, may contain text information printed in more than one script/language forms. For
such multi script documents, it is necessary to pre-determine the language type of the document,
before employing a particular OCR on them. With this context, it is proposed to work on the
prioritized requirements of a particular region- Karnataka, a state in India.
In a multi-lingual country like India (India has 18 regional languages derived from 12
different scripts; a script could be a common medium for different languages), documents like
bus reservation forms, passport application forms, examination question papers, bank-challenge,
language translation books and money-order forms may contain text words in more than one
language forms. For such an environment, multi lingual OCR system is needed to read the
multilingual documents. To make a multi-lingual OCR system successful, it is necessary to
separate portions of different language regions of the document before feeding to individual
OCR systems. In this direction, multi lingual document segmentation has strong direct
application potential, especially in a multilingual country like India. In the context of Indian
languages, some amount of research work has been reported. Further there is a growing demand
for automatically processing the documents in every state in India including Karnataka. Under
the three language formulae, adopted by most of the Indian states, the document in a state may
be printed in its respective official regional language, the national language Hindi and also in
English. Accordingly, a document produced in Karnataka, a state in India, may be printed in its
official regional language Kannada, national language Hindi and also in English. For such an
environment, multilingual OCR system is needed to read the multilingual documents.
According to the three language policy adopted by most of the Indian states, the
documents produced in Karnataka are composed of texts in Kannada- the regional language,
Hindi – the National language and English. Such trilingual documents are found in majority of
the private and Government sectors, railways, airlines, banks, post-offices of Karnataka state.
For automatic processing of such tri-lingual documents through the respective OCRs, a pre-
processor is necessary which could identify the language type of the texts words. So, it is
proposed to develop a model to identify the script of documents containing Kannada, Hindi and
English text.
Some essential factors need to be considered before choosing or designing a script
identification scheme for any multi-lingual application. These factors are: (a) complexity in pre-
processing, (b) complexity in feature extraction and classification, (c) computational speed of
entire scheme, (d) sensitivity of the scheme to the variation in text in document (font style, font
size and document skew), (e) performance of the scheme, and (f) range of applications in which

609
the scheme could be used. Performance of the scheme includes accuracy reported and selection
of testing data. Currently, individual approaches are designed such that they can effectively deal
with some of the factors listed above (not all).
Some of the key challenges identified in script identification works [1-10] from the
factors listed above are presence of document degradation, skew, varying font size and font type.
There are four types of most common document degradation, namely, poor image resolution,
noise including salt and pepper noise, and Gaussian noise and physical document degradation.
All these document degradation must be compensated before script identification. An image that
is slanting too far in one direction or one that is misaligned is known as skew. Compensating for
the dominant skew angle in an entire page image may not be sufficient adjustment to allow
accurate script identification. In case of varying font size and font type, the relative offsets are
distributed it is difficult to accurately estimate results with limited font size. It is difficult to
classify documents those printed in unfamiliar font types. The difficulty of most of images in
script identification appeared to stem from their unfamiliar font types.
From the reported works [1-10] on script identification, the documents produced in
Karnataka usually are composed of texts in Kannada, Hindi and English. Though a great amount
of work has been carried out on identification of the three languages Kannada, Hindi and
English, very few works pertain to script identification processing the document image at
word/line level. By analysing the study of work carried out on word level identification of
Kannada, English and Hindi, a generalisation of existing work with more accurate results for
script identification from document images have been carried out. Also, the processing of
word/line level reduces the number of computations.
Language identification is one of the vision application problems. Generally human
system identifies the language in a document using some visible characteristic features such as
texture, horizontal lines, vertical lines, which are visually perceivable and appeal to visual
sensation. This human visual perception capability has been the motivator for the development of
the proposed system. With this context, an attempt has been made to simulate the human visual
system, to identify the type of the script based on visual clues, without reading the contents of
the document. . Hence, this motivated for developing a technique for script identification of
Kannada, Hindi and English from printed document images used in Karnataka to report better
recognition.
In this work a technique for script identification of Kannada, English and Hindi from
document images is proposed. In this work a technique for script identification from document
images is proposed. The method uses vertical and horizontal run components/objects of words of
a single line of text to identify the script of the document image. Further, the method
distinguishes 3 Indian scripts: Kannada, Hindi and English. Initially, the method segments words
from the selected line of text from a document image. Then statistics of horizontal and vertical
run objects are determined. Further, linear discriminant function is used to identify script of the
document image as Kannada, Hindi or English script. The method has been tested for 300
document images and the method found to be robust and efficient. The proposed system achieves
93% identification accuracy for Hindi script, 90% identification accuracy for English script and
86% identification accuracy for Kannada script. The literature survey related to current work is
summarized in the following section.

610
The rest of the paper is organized as follows; the detailed survey related to script
identification from printed document images is described in Section 2. The proposed method is
presented in Section 3. The experimental results and discussions are given in Section 4. Section
5concludes the work and lists future directions of the work.
2. RELATED WORKS
A substantial amount of work has gone into the research related to script identification
from printed document images. Some of the related works are summarized in the following.
A robust method for determination of the script and language content of Document
Images proposed in [1]. The algorithm determines connected components and locates upward
concavities and then classifies the script into two broad classes Han-based (Chinese, Japanese
and Korean) and Latin-based (English, French, German and Russian) languages. The extraction
of Rotation Invariant Texture Features and Their Use in Automatic Script Identification has been
carried out in [2]. The method computes features from text blocks using multi-channel gabor
filters, constructs a representative feature vector and Euclidian distance classifier is used for
script identification of 6 languages (Chinese, English, Greek, Russian, Persian, and Malayalam).
Script and Language Identification from Document Images using Multiple channel Gabor filters
and gray level cooccurrence matrices (GLCMs) to extract texture features and K-NN classifier is
used to classify seven languages; Chinese, English, Greek, Korean, Malayalam, Persian and
Russian has been proposed in [3].
The Cluster-Based Templates is used for Automatic Script Identification from Document
Images in [4]. Evaluation of Texture features for Script Identification is carried out in [5]. A
method for Automatic Identification of English, Chinese, Arabic, Devnagari and Bangla Script
Line is discussed in [6]. A method for Script and Language Identification in Noisy and Degraded
Document Images is presented in [7]. Script Identification Based on Morphological
Reconstruction in Document Images is described in [8]. A simple technique based on the
characteristic features of top-profile and bottom-profile of individual text lines for Identification
for Kannada, Hindi and English text lines from a printed document is proposed in [9]. Script
Identification at both paragraph and word level using Appearance based models have been
presented in [10].
A Survey of Script Identification technique for Multi-Script Document Images is carried out
in [11]. Two-stage Approach for Word-wise Script Identification of English (Roman), Devnagari and
Bengali (Bangla) scripts is proposed in [12]. Zone-based Structural feature extraction to recognize
four south Indian scripts namely Kannada, Telugu, Tamil and Malayalam along with English and
Hindi is employed in [13]. A technique presented in [14] use Voting Technique for Script
Identification from a Tri Lingual Document. The technique presented in [15] extracts features
consistent with human perception from the responses of a multi-channel log-Gabor filter bank,
designed at an optimal scale and multiple orientations for Script Identification from Indian
Documents.
A simple and efficient technique for script identification for Kannada, Hindi and English text
lines from a printed document using horizontal projection profile is presented in [16]. A method for
Word level Script Identification for scanned document images in which during both training and
testing , a Gabor filter is applied and 16 channels of features are extracted is evaluated in [17]. Multi-

611
script identification technique for Indian languages using different text lines of Indian scripts from a
document are identified in [18].
A method found in [19] uses texture-based approach to identify the script type using Wavelet
Packet Based Features for documents printed in seven scripts: Kannada, Tamil, Telugu, Malayalam,
Urdu, Hindi and English.
A technique proposed in [20] for language identification in document images to discriminate
five major Indian languages: Hindi, Marathi, Sanskrit, Assamese and Bengali belong to Devnagari
and Bangla scripts. But, in the current work horizontal and vertical run objects determined from the
text line of document image are used to determine the script of document. The detailed description of
the methodology is given in the following section.
3. PROPOSED METHODOLOGY FOR SCRIPT IDENTIFICATION
The proposed methodology uses horizontal and vertical run objects to determine the script of
the document image containing Kannada, Hindi or English text. The methodology comprises four
phases; Image Acquisition, Preprocessing, Segmentation, Feature Extraction and Linear Discriminant
Analysis. The block diagram of proposed model is given in Figure 3a. The detailed description of
each processing step is presented in the following subsections.
3.1 Image acquisition
The process begins with acquiring document images of the three scripts Kannada, Hindi and
English. The document images are scanned images which are downloaded from the internet. The
document images considered as input are skew free and noise free. About 300 sample images i.e.,
100 samples of each script are collected as requirement.
Input document image
Identified script as Kannada/English/Hindi
Fig. 3a. Block Diagram of Proposed Model
PREPROCESSING
(Binarization and Bounding Box )
SEGMENTATION
(Line and Words segmentation)
FEATURE EXTRACTION
(Horizontal run objects and Vertical run objects)
LINEAR DISCRIMINANT ANALYSIS

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3.2 Preprocessing
In the preprocessing phase, the text document images taken as input are binarized and
bounding box is generated. Binarization is the step in which the image is converted into binary
image where each pixel is represented by either 0 or 1. Binary image is a black and white type of
image. Bounding box is generated by applying horizontal and vertical run objects. The purpose
of this phase is to make the image easier for the feature extraction and classification.
3.3. Segmentation
In this phase the segmentation of single line from the document image is carried out. The
bounding box is generated around the segmented line. From the selected line, the words are
segmented and bounding boxes are generated to the segmented words. The segmentation process
of line and words is described below.
• Segmentation of line
The horizontal projection features are determined to segment a line from the document
image. Bounding box is generated to the segmented line. The line segmentation of Hindi script is
as shown in below Figure 3b, the English script is Figure 3c and the Kannada script is Figure 3d.
3b
3c
3d
Fig. 3b, c, d. Sample Images of segmented lines of Hindi, English and Kannada script
• Segmentation of words
The vertical projection features are determined to extract words from the selected lines.
Using the boundary between two consecutive vertical projections, the words are segmented.
Then the bounding box’s are generated to the segmented words. The segmented words of above
Figures 3b, 3c and 3d are given in the below Figures 3e, 3f and 3g respectively.

613
3e 3f 3g
Fig 3e, f, g. Sample Images of Segmented words of Hindi, English and Kannada lines.
3.4. Feature extraction
In this phase, the Horizontal run object and vertical run objects of each segmented text
words are determined.
Horizontal run object
In the binary image of each text word, a set of consecutive pixels in a row whose length
is greater than the threshold value (HT) results in a horizontal run objects.
Vertical run object
In the binary image of each text word, a set of consecutive pixels in a column whose
length is greater than the threshold value (VT) results in a vertical run objects. The number of
horizontal and vertical run objects are determined and stored into a feature vector Fv as given in
equation (1).
(1)
Where, Fv is Feature Vector
is the number of horizontal run objects
is the number of vertical run objects

614
3.5. Linear Discriminant Analysis
The Discriminant analysis phase of the proposed model uses the and features to
classify the segmented words of the document image as Hindi, Kannada or English script.
Condition 1:
If one of the horizontal run objects ( ) in a word is greater than half of the number of
columns(n2) in a word then, the script of the word is identified as Hindi. (HT is n2/2)
> (n2/2) = Word is Hindi script (2)
Condition 2:
If the value of feature is greater the value of feature , then the script of the word
is identified as Kannada. (HT considered is 3 and VT considered is 5)
> = Word is Kannada script (3)
Condition 3:
Else if the value of feature is greater the value of feature then, the script of the
word is identified as English. (HT considered is 3 and VT considered is 5)
> = Word is English script (4)
After identifying the script of each segmented words then the classification of script of
the document image is done on the bases of above conditions.
Condition 4:
If from the selected line in the document image, the number of words identified as Hindi
script i.e. equation (2) is greater than the total number of words in the selected line then, the
script of the document image is identified as Hindi script.
Condition 5:
If the document image is not Hindi script, then if from the selected line the text words
identified as Kannada script i.e. equation(3) are greater than or equal to the words identified as
English script i.e. equation(4) from the selected line, then the script of the document image is
identified as Kannada script.
Condition 6:
Else, if the document image is not Kannada script, then it means the text words from the
selected line identified as English script i.e. equation (4) are greater than the words identified as
Kannada script i.e. equation (3). And hence, the script of the document image is identified as
English script.

615
4. EXPERIMENTAL RESULTS AND DISCUSSION
For the purpose of experimentation we have created our own database of document
images. The document images are scanned images which are downloaded from the internet. The
document images considered as input are skew free and noise free. About 300 sample images
i.e., 100 samples of each script are collected as requirement. The proposed methodology has
been tested for about 300 document images containing Kannada, Hindi and English script.
Horizontal and vertical run objects are used for feature extraction. Further, linear discriminant
Analysis is carried out to identify the script of the document image as Hindi, Kannada or English
script. The documents having different font sizes have been considered. Exhaustive
experimentations were done to analyze the performance of the system for different image
patterns.
4.1. An Experimental Analysis for a Sample Hindi Document Image.
Fig. 4a. Sample Input Document Image
Figure 4.a shows sample input document image. The bounding box and Binarization of
input document image is done. The segmentation of line from the document image is carried out.
The segmented line from the document image is shown in the Figure 4.b
Fig. 4b. Segmented Line from Input Image

616
After segmentation of line the words are segmented from the selected line. The
segmented words from the line in Figure 4.b are given in Figure 4.c
Fig. 4c. Segmented words
Feature extraction and Linear Discriminant Analysis is carried out. And finally the
document image is identified as Hindi script. The Figure 4.d shows the result displayed.
Fig. 4d. Dialog box
4.2. An Experimental Analysis for a Sample English Document Image.
Example 2: English sample
Fig. 4e. Sample English Input document image

617
Figure 4.e shows the original English document image. After applying bounding box and
binarization of the image, segmentation of line from the document image is carried out. The
segmented line from the document image is shown in the Figure 4.f.
Fig. 4f. Segmented line
After segmentation of line the words are segmented from the selected line. The
segmented words are given in Figure 4.g
Fig. 4g. Segmented words
Feature extraction and Linear Discriminant Analysis is carried out. And finally the
document image is classified as English script. The Figure 4.h shows the result displayed.
Fig. 4h. Dialog box
4.3 System Performance Analysis
The overall system performance of the script identification from printed document
images is as shown in the below Table 1
Table 1: Overall System Performance
Tested scripts Number of
document images
Classification rate
Word wise
Classification rate
Line wise
Hindi script 100 (987/1053) 94% 93%
Kannada script 100 (496/636) 78% 86%
English script 100 (781/936) 83% 90%

618
4.4 An Experimental Analysis dealing with various issues
The proposed methodology has been evaluated dealing with various issues such as
variation in font size and style, color, noise, varying spacing between words. The results of
experimentation are given below;
Example 1: Sample image with containing noisy document image.
Fig. 4i. Input document image
Fig. 4j. Segmented line
Fig 4k. Extracted words

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Fig. 4l. Dialog box
Example 2: Sample image with smaller font size
Fig. 4m. Input document image
Fig. 4n. Segmented line

620
Fig. 4o. Segmented words
Fig. 4p. Dialog box
5. CONCLUSION
In this method, Line and Word-Wise identification models to identify Kannada, Hindi
and English text words from Indian multilingual machine printed documents have been
presented. The proposed model is developed based on the visual discriminating features, which
serve as useful visual clues for script identification. Horizontal and Vertical run objects are used
for feature extraction. The methods help to accurately identify and separate different language
portions of Kannada, English and Hindi. The experimental results show that the method is
effective and good enough to identify and separate the three language portions of the document,
which further helps to feed individual language regions to specific OCR system. Further, linear
discriminant function is used to identify script of the document image as Kannada, Hindi or
English script. The method has been tested for 300 document images and the method found to be
robust and efficient. The proposed system achieves 93% identification accuracy for Hindi script,
90% identification accuracy for English script and 86% identification accuracy for Kannada
script approach. The proposed system can also be extended to identify other Indian languages
and foreign languages.
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