Script Identification of Multi-Script Documents: A Survey <s> I. INTRODUCTION <s> Research and development of OCR systems are considered from a historical point of view. The historical development of commercial systems is included. Both template matching and structure analysis approaches to R&D are considered. It is noted that the two approaches are coming closer and tending to merge. Commercial products are divided into three generations, for each of which some representative OCR systems are chosen and described in some detail. Some comments are made on recent techniques applied to OCR, such as expert systems and neural networks, and some open problems are indicated. The authors' views and hopes regarding future trends are presented. > <s> BIB001 </s> Script Identification of Multi-Script Documents: A Survey <s> I. INTRODUCTION <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> I. INTRODUCTION <s> A variety of different scripts are used in writing languages throughout the world. In a multiscript, multilingual environment, it is essential to know the script used in writing a document before an appropriate character recognition and document analysis algorithm can be chosen. In view of this, several methods for automatic script identification have been developed so far. They mainly belong to two broad categories-structure-based and visual-appearance-based techniques. This survey report gives an overview of the different script identification methodologies under each of these categories. Methods for script identification in online data and video-texts are also presented. It is noted that the research in this field is relatively thin and still more research is to be done, particularly in the case of handwritten documents. <s> BIB003 </s> Script Identification of Multi-Script Documents: A Survey <s> I. INTRODUCTION <s> Recognition of text in natural scene images is becoming a prominent research area due to the widespread availablity of imaging devices in low-cost consumer products like mobile phones. To evaluate the performance of recent algorithms in detecting and recognizing text from complex images, the ICDAR 2011 Robust Reading Competition was organized. Challenge 2 of the competition dealt specifically with detecting/recognizing text in natural scene images. This paper presents an overview of the approaches that the participants used, the evaluation measure, and the dataset used in the Challenge 2 of the contest. We also report the performance of all participating methods for text localization and word recognition tasks and compare their results using standard methods of area precision/recall and edit distance. <s> BIB004 </s> Script Identification of Multi-Script Documents: A Survey <s> I. INTRODUCTION <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> BIB005
Our reliance on the digital world is continuously increasing with the rapid developments in information and technology in all aspects of our lives. In administrative and office environments, the development of Optical Character Recognition (OCR) systems have lightened the possibility of creating paperless solutions. Unfortunately, although the field of OCR has been one of the oldest and more investigated research fields, still today OCR systems are just specialized in one particular script. In order to overcome this limitation, script identification has been used. In fact, in a multi-script document image, script identification is necessary to find text portions written in the same script, so that script-specific OCR system can be applied. Hence, script identification system is one of the most important components in multiscript document image analysis and it is used for a wide range of applications such as automatic storage of multi-script document images, document image retrieval, video indexing and retrieval, document sorting in digital libraries BIB003 , BIB001 . Spitz BIB002 carried out the first extensive research on automatic script identification in 1994. Successively, two comprehensive surveys were conducted by Ghosh and Shivaprasad in 2000 BIB005 and Pal in 2006  . Moreover, a survey specifically devoted to Indian script identification was published by Pal and Chaudhuri in 2004 [77] . It is worth noting that many researches on script identification have been devoted to Indian scripts since script identification is essential in a multi-lingual, multi-script country like India, where 18 official Indian languages and 12 different scripts are used. Documents are printed in three languages: English, Hindi (Devnagari) and the official regional language. More recently, several international competitions were also performed on script identification tasks  , BIB004 . This paper presents a comprehensive survey of different script identification techniques. Section II gives a brief description of different writing systems. Section III introduces different document types and highlights the main discriminating methods. Section IV and V discuss the most profitable features and the classification techniques for script identification, respectively. System performances are analyzed in Section VI. Section VII presents some valuable trends for future research. Section VIII reports the conclusions of the paper.