Script Identification of Multi-Script Documents: A Survey <s> 1) PAGE/PARAGRAPH/TEXT BLOCK LEVEL SCRIPT IDENTIFICATION <s> We describe an automated script identification system for typeset document images. Templates for each script are created by clustering textual symbols from a training set. Symbols from new images are compared to the templates to find the best script. Our current system processes thirteen scripts with minimal preprocessing and high accuracy. <s> BIB001 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) PAGE/PARAGRAPH/TEXT BLOCK LEVEL SCRIPT IDENTIFICATION <s> Most document recognition work to date has been performed on English text. Because of the large overlap of the character sets found in English and major Western European languages such as French and German, some extensions of the basic English capability to those languages have taken place. However, automatic language identification prior to optical character recognition is not commonly available and adds utility to such systems. Languages and their scripts have attributes that make it possible to determine the language of a document automatically. Detection of the values of these attributes requires the recognition of particular features of the document image and, in the case of languages using Latin-based symbols, the character syntax of the underlying language. We have developed techniques for distinguishing which language is represented in an image of text. This work is restricted to a small but important subset of the world's languages. The method first classifies the script into two broad classes: Han-based and Latin-based. This classification is based on the spatial relationships of features related to the upward concavities in character structures. Language identification within the Han script class (Chinese, Japanese, Korean) is performed by analysis of the distribution of optical density in the text images. We handle 23 Latin-based languages using a technique based on character shape codes, a representation of Latin text that is inexpensive to compute. <s> BIB002 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) PAGE/PARAGRAPH/TEXT BLOCK LEVEL SCRIPT IDENTIFICATION <s> Two types of techniques are usually adopted in language differentiation: token matching and statistical analysis. In this paper we present a method which uses a combined analysis of several discriminating statistical features for the differentiation between European and oriental language scripts. When applied to more than 23 languages, it has proved to be effective in classifying documents printed in these different scripts. <s> BIB003 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) PAGE/PARAGRAPH/TEXT BLOCK LEVEL SCRIPT IDENTIFICATION <s> The extraction of textual information from images and videos is an important task for automatic content-based indexing and retrieval purposes. To extract text from images or videos coming from unknown international sources, it is necessary to know the script beforehand in order to employ suitable text segmentation and optical character recognition (OCR) methods. In this paper, we present an approach for discriminating between Latin and Ideographic script. The proposed approach proceeds as follows: first, the text present in an image is localized. Then, a set of low-level features is extracted from the localized text image. Finally, based on the extracted features, the decision about the type of the script is made using a k-nearest neighbour classifier. Initial experimental results for a set of images containing text of different scripts demonstrate the good performance of the proposed solution <s> BIB004 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) PAGE/PARAGRAPH/TEXT BLOCK LEVEL SCRIPT IDENTIFICATION <s> Script identification for handwritten document image is an open document analysis problem. In this paper, we propose an approach to script identification for documents containing handwritten text using the texture features. The texture features are extracted based on the co-occurrence histograms of wavelet decomposed images, which capture the information about relationships between each high frequency subband and that in low frequency subband of the transformed image at the corresponding level. The correlation between the subbands at the same resolution exhibits a strong relationship, indicating that this information is significant for characterizing a texture. This scheme is tested on seven Indian language scripts alongwith English. Our method is robust to the skew generated in the process of scanning a document and also to the varying coverage of text. The experimental results demonstrate the effectiveness of the texture features in identification of handwritten scripts. The experiments are also performed by considering the multiple writers. <s> BIB005 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) PAGE/PARAGRAPH/TEXT BLOCK LEVEL SCRIPT IDENTIFICATION <s> One very important field of application of pattern recognition is character recognition. But, until now, almost all character-recognition systems make an implicit assumption that the script and/or the language of the document to be processed is known. This may not be true in an intenational environment. So, script identification plays an importantrole in automatic processing of document images. In this paper, we present a new scdpt identification algorithm that can identify the same from the characters in a handwritten document. The proposed scheme is based on training viaclustering and classification using possibilistic membership grades of a character to all the script classes. This enablesthe recognizer to work with ambiguous and uncertain cases. The recognizer may reject a pattern when it can not make any clear decision ensuring high reliability. Simulation results confirm the effectiveness of the. proposed script identification technique <s> BIB006
Most research on printed document script identification has been carried out at the page level. Hochberg et al. BIB001 used cluster-based templates for discriminating 13 different scripts. Spitz BIB002 proposed a language identification scheme where the words of 26 different languages were first classified into Han-based and Latin-based scripts. Successively, the actual languages were identified using projection profiles of words and character shapes. Jie Ding et al. BIB003 presented a method which uses a combined analysis of several discriminating statistical features to categorize European and Oriental language scripts. Chaudhuri and Pal  developed a system for identifying Bangla and Devnagari (Hindi) scripts using a classification tree. Research on printed document script identification was also conducted at text block level. For instance, Peake and Tan  proposed a method based on Multiple Gabor filters and grey level co-occurrence matrices to extract the texture features of five major scripts. The first study conducted on handwritten script identification was carried out by Chaudhuri  and was similar to that proposed by Hochberg et al. BIB001 for printed documents. However, the resulting classification accuracy was lower than that for the printed documents. An online handwritten script recognition system was proposed by Namboodiri and Jain  for classifying six major scripts at word level. Eleven different features and six types of classifiers were considered. A method based on the texture features for script identification in a handwritten document image was proposed by Hiremath et al. BIB005 . Ghosh and Shivaprasad BIB006 proposed an handwritten script identification method in which a ''possibilistic'' approach was used for cluster analysis. Gllavata and Freisleben BIB004 presented an approach for discriminating between Latin and Ideographic scripts by a set of low-level features. The decision is made using a K-Nearest Neighbour classifier. New Spatial-Gradient based Features (SGF) were proposed by Zhao et al.  for script identification at block level for six scripts namely, Arabic, Chinese, English, Japanese, Korean and Tamil.