Script Identification of Multi-Script Documents: A Survey <s> 2) TEXT-LINE LEVEL SCRIPT IDENTIFICATION <s> In a general situation, a document page may contain several scriptforms. For optical character recognition (OCR) of such a document page, it is necessary to separate the scripts before feeding them to their individual OCR systems. An automatic technique for the identification of printed Roman, Chinese, Arabic, Devnagari and Bangla text lines from a single document is proposed. Shape based features, statistical features and some features obtained from the concept of a water reservoir are used for script identification. The proposed scheme has an accuracy of about 97.33%. <s> BIB001 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) TEXT-LINE LEVEL SCRIPT IDENTIFICATION <s> In a multi-lingual country like India, a document page may contain more than one script form. Under the three-language formula, the document may be printed in English, Devnagari and one of the other Indian official languages. For Optical Character Recognition (OCR) of such a document page, it is necessary to separate these three script forms before feeding them to the OCRs of individual scripts. In this paper an automatic technique of separating the text lines is presented for almost all triplet of script forms. To do so, the triplets are grouped into five classes according to their characteristics, and shape based features have been employed to separate them without any expensive OCR-like algorithms. The proposed approaches are tested on many documents and the experimental results are presented. At present, the system has an overall accuracy of about 98.5%. <s> BIB002 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) TEXT-LINE LEVEL SCRIPT IDENTIFICATION <s> A document page may contain two or more different scripts.For Optical Character Recognition (OCR) of such adocument page, it is necessary to separate different scriptsbefore feeding them to their individual OCR system. In thispaper an automatic scheme is presented to identify text linesof different Indian scripts from a document. For theseparation task at first the scripts are grouped into a fewclasses according to script characteristics. Next featurebased on water reservoir principle, contour tracing, profileetc. are employed to identify them without any expensiveOCR-like algorithms. At present, the system has an overallaccuracy of about 97.52%. <s> BIB003 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) TEXT-LINE LEVEL SCRIPT IDENTIFICATION <s> In Japanese documents, a single text line of a page may contain both Japanese and English scripts. For the optical character recognition of such a document page it is better to identify Japanese and English script portions at first, and then to use individual OCRs of these two scripts on their respective identified portions to get higher OCR accuracy. In this paper, an automatic technique for identification of Japanese and English script portions from a single line of a printed document page is proposed. To the best of our knowledge this is the first work of its kind. Here, at first, the document is segmented into lines and then lines are segmented into characters. In the proposed scheme, individual scripts are identified using combination of different features obtained from structural shape of characters, pitch information, topological properties, water reservoir concept etc. Based on the experiment on 11304 characters, we obtained 98.79% identification accuracy from the proposed scheme. <s> BIB004 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) TEXT-LINE LEVEL SCRIPT IDENTIFICATION <s> In a multi-script multi-lingual environment, a document may contain text lines in more than one script/language forms. It is necessary to identify different script regions of the document in order to feed the document to the OCRs of individual language. With this context, this paper proposes to develop a monothetic algorithmic model to identify and separate text lines Telugu, Hindi and English scripts from a printed multilingual document. The proposed method uses the distinct features of the target script and searches for the text lines that possess the anticipated features. Experimentation conducted involved 1500 text lines for learning and 900 text lines for testing. The performance has turned out to be 98.5%. <s> BIB005 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) TEXT-LINE LEVEL SCRIPT IDENTIFICATION <s> India is a multilingual multi-script country. States of India follow a three language formula. The document may be printed in English, Hindi and other state official language. For example in Karnataka, a state in India, the document may contain text lines in English, Hindi script. For Optical Character Recognition (OCR) of such a multilingual document, it is necessary to identify the script before feeding the text lines to the OCRs of individual scripts. In this paper, a simple and efficient technique of script identification for Kannada, Hindi and English text lines from a printed document is presented. The proposed system uses horizontal projection profile to distinguish the three scripts. The feature extraction is done based on the horizontal projection profile of each text line. The knowledge base of the system is developed based on 15 different document images containing about 450 text lines. For a new text line, necessary features are extracted from the horizontal projection profile and compared with the stored knowledge base to classify the script. The proposed system is tested on 20 different document images containing about 200 text lines of each script and an overall classification rate of 99.83% is achieved. <s> BIB006 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) TEXT-LINE LEVEL SCRIPT IDENTIFICATION <s> In this paper, we present a new method for video script identification which is essential before choosing an appropriate OCR engine for identifying text lines when a video frame contains more than one language. The input for script identification is the text lines obtained by our text detection method. We extract upper and lower extreme points for each connected component of Canny edges of text lines. The extracted points are connected to study the behavior of upper and lower lines. The direction of each 10-pixel segment of the lines is determined using PCA. The average angle of the segments of the upper and lower lines is computed to study the smoothness and cursiveness of the lines. In addition, to discriminate the scripts accurately, the method divides a text line into five equal zones horizontally to study the smoothness and cursiveness of the upper and lower lines of each zone. We evaluate the method by conducting experiments on different combinations of languages such as English and Chinese, English and Tamil, Chinese and Tamil, and English, Chinese and Tamil. <s> BIB007 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) TEXT-LINE LEVEL SCRIPT IDENTIFICATION <s> Script identification is an important step in multi-script document analysis. As different textures present in text portion of a script are the main distinct features of the script, in this paper, we proposed a new algorithm for printed script identification based on texture analysis. Since local patterns is a unifying concept for traditional statistical and structural approaches of texture analysis, here the basic idea is to use the histogram of the local patterns as description of the script stroke directions distribution which is the characteristic of every script. As local pattern, the basic version of the Local Binary Patterns (LBP) and a modified version of the Orientation of the Local Binary Patterns (OLBP) are proposed. A Least Square Support Vector Machine (LS-SVM) is used as identifier. The scheme has been verified on two databases. The first or training database is a database with 200 sheets of 10 different scripts. The scripts font is provided by the Google translator. The second or test database has been obtained by scanning different newspapers and books. It contains 5 common scripts among 10 different scripts of the first database. From the experiment we obtained encouraging results. <s> BIB008
In text-line level script identification, a text block is firstly divided into lines. Pal and Chaudhuri BIB002 developed an automatic technique for separating text lines using script characteristics and shape based features. They also proposed a system for the identification of printed Roman, Chinese, Arabic, Devnaguri and Bangla text lines from a single document BIB001 and a method of identifying text lines of different Indian scripts from a document BIB003 . An automatic technique for the identification of Japanese and English script portions from a single line of a printed document was proposed by Chanda et al. BIB004 . Padma and Vijaya BIB005 developed a monothetic algorithmic model to identify and separate Telugu, Hindi and English text lines from a printed multilingual VOLUME 5, 2017 document. A simple and efficient technique of script identification for Kannada, Hindi and English text lines was presented by Prakash et al. BIB006 . Ferrer et al. BIB008 proposed a LBP-based line-wise script identification system to identify ten different scripts. Namboodiri and Jain  proposed a method to classify words and lines into one of the six major scripts: Arabic, Cyrillic, Devnagari, Han, Hebrew or Roman. The classification is based on eleven different spatial and temporal features extracted from strokes of the words. Phanet al. BIB007 proposed two features, namely smoothness and cursiveness, for video script identification at text-line level. In their approach, English, Chinese and Tamil scripts were considered.