Script Identification From Printed Document Images Using Statistical

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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. Karjol

2

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. Inthis 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 wordsfrom 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 and86% identification accuracy for Kannada script.

1. INTRODUCTION

In recent years, the escalating use of physical documents has made progress towards thecreation 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

INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING

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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, museumsstore 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 toprovide 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 aparticular 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. Forsuch 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 onelanguage 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 individualOCR 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. Underthe 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 inEnglish. 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, thedocuments 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 isproposed to develop a model to identify the script of documents containing Kannada, Hindi andEnglish 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 ofentire 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


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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 dealwith 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 forthe 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 toclassify 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 inKarnataka 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 ofKannada, 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 ofthe 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 ofthe 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 fromdocument 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 methoddistinguishes 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 verticalrun objects are determined. Further, linear discriminant function is used to identify script of thedocument 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 issummarized in the following section.


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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 ispresented 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, Japaneseand 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 gaborfilters, 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 isused 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 DocumentImages 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 MorphologicalReconstruction 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 Identificationfor 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 featuresconsistent 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-


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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 IDENTIFICATIONThe 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 DiscriminantAnalysis. 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 acquisitionThe 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 PreprocessingIn 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 purposeof 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. Thebounding 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 lineThe 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 wordsThe 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 boxs 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.


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3e 3f 3g

Fig 3e, f, g. Sample Images of Segmented words of Hindi, English and Kannada lines.

3.4. Feature extractionIn 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 lengthis greater than the threshold value (HT) results in a horizontal run objects.

Vertical run objectIn 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 ofhorizontal and vertical run objects are determined and stored into a feature vector Fv as given inequation (1).

(1)Where, Fv is Feature Vector

is the number of horizontal run objects

is the number of vertical run objects


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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 ofcolumns(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 wordis 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 theword 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, thescript 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 theselected 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.


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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 imagesi.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 Englishscript. 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


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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 thedocument 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


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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. Thesegmented 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 thedocument image is classified as English script. The Figure 4.h shows the result displayed.

Fig. 4h. Dialog box

4.3 System Performance AnalysisThe 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%


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4.4 An Experimental Analysis dealing with various issues

The proposed methodology has been evaluated dealing with various issues such asvariation 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


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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, whichserve 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 berobust and efficient. The proposed system achieves 93% identification accuracy for Hindi script,

90% identification accuracy for English script and 86% identification accuracy for Kannadascript approach. The proposed system can also be extended to identify other Indian languages

and foreign languages.

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