Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <s> In a full open end closure for a container having a peripheral score defining a removable section in the panel of the end closure, a novel combination of structural features is provided to stiffen the portion of the panel encompassing the peripheral score. The peripheral score is comprised by a pop section at the forward end of the removable section which merges into adjoining start tear sections of the peripheral score, a detach section at the rearward end of the removable section which merges into adjoining minimum tear sections of the peripheral score, and full diameter tear sections at the sides of the removable section which merge into adjoining start tear and minimum tear sections. The end panel also has a hinge score adjacent to the pop section of the peripheral score and a securement for attaching an opening tab to the removable section of the panel, the attachment being positioned between the peripheral score and the hinge score. The novel structural features in the panel are comprised by an inner score in the removable section which runs substantially parallel to the peripheral score, has a depth shallower than that of the peripheral score, and adjoins and merges at its ends into the ends of the hinge score, and stiffening means in the removable section in the form of a ridgelike feature projecting outwardly from the removable section between the peripheral and inner scores both at the forward and rearward ends of the removable section. The novel combination of the inner score with the stiffening means provide a line along the inner score about which the central portion of the removable section is allowed to flex when subjected to forces directed against the removable section to thereby reduce the susceptibility of the peripheral score to abuse and premature fracture which would occur if the removable section should flex on a line along the peripheral score. <s> BIB001 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <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> BIB002 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <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> BIB003 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <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 differentiating between documents printed in these different scripts. <s> BIB004 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <s> Automatic processing of international documents presents a number of challenging problems because Optical Character Recognition (OCR) techniques are not available for all languages and all script classes. Document images must be categorized according to their script type first, in our case Roman, Ideographic, or Arabic. We present a set of statistical methods that first detect and correct the skew of a document image. Next, the page is segmented into text and graphical components. The textual components are then segmented into paragraphs and lines; and finally we classify the script type into one of three categories. The system predicts the correct script category in 91% of cases when tested on real-life documents of varying kinds, diverse formats and qualities from many sources. <s> BIB005 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <s> Identification of the script in an image of a document page is of primary importance for a system processing multi-lingual documents. In this paper three trainable classification schemes have been proposed for identification of Indian scripts. The first scheme is based upon a frequency domain representation of the horizontal profile of the textual blocks. The other two schemes use connected components extracted from the textual region. We have proposed a novel Gabor filter-based feature extraction scheme for the connected components. We have also found that frequency distribution of the width-to-height ratio of the connected components can also be used for script recognition. It has been experimentally found that the Gabor filter-based scheme provides the most reliable performance. However, the other two techniques are computationally more efficient. <s> BIB006 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <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> BIB007 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <s> Identification of script in multi-lingual documents is essential for many language dependent applications suchas machine translation and optical character recognition. Techniques for script identification generally require large areas for operation so that sufficient information is available. Such assumption is nullified in Indian context, as there is an interspersion of words of two different scripts in most documents. In this paper, techniques to identify the script of a word are discussed. Two different approaches have been proposed and tested. The first method structures words into 3 distinct spatial zones and utilizes the information on the spatial spread of a word in upper and lower zones, together with the character density, in order to identify the script. The second technique analyzes the directional energy distribution of a word using Gabor filters with suitable frequencies and orientations. Words with various font styles and sizes have been used for the testing of the proposed algorithms and the results obtained are quite encouraging. <s> BIB008 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <s> Abstract For wider readership, some documents may be printed in several scripts and languages. For optical character recognition (OCR) of such a document page, a software module is necessary to identify the scripts before feeding them to their individual OCR systems. This paper deals with an automatic technique for the identification of printed Roman, Chinese, Arabic, Devnagari and Bangla text lines from a single document. For this purpose script characteristics, shape-based features, statistical features and some features obtained from the concept of water overflow from the reservoir have been employed. The scheme shows an accuracy of about 97.33%. <s> BIB009 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <s> This paper deals with an Optical Character Recognitionsystem for printed Urdu, a popular Indian script. Thedevelopment of OCR for this script is difficult because (i) alarge number of characters have to be recognized (ii) thereare many similar shaped characters. In the proposedsystem individual characters are recognized using acombination of topological, contour and water reservoirconcept based features. The feature detection methods aresimple and robust. A prototype of the system has beentested on printed Urdu characters and currently achieves97.8% character level accuracy on average. <s> BIB010 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <s> Abstract Intensive research has been done on optical character recognition (OCR) and a large number of articles have been published on this topic during the last few decades. Many commercial OCR systems are now available in the market. But most of these systems work for Roman, Chinese, Japanese and Arabic characters. There are no sufficient number of work on Indian language character recognition although there are 12 major scripts in India. In this paper, we present a review of the OCR work done on Indian language scripts. The review is organized into 5 sections. Sections 1 and 2 cover introduction and properties on Indian scripts. In Section 3, we discuss different methodologies in OCR development as well as research work done on Indian scripts recognition. In Section 4, we discuss the scope of future work and further steps needed for Indian script OCR development. In Section 5 we conclude the paper. <s> BIB011 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <s> Postal automation is a topic of research over the last few years. There are many works towards the postal automation in USA, UK, Japan and Australia, but for Indian postal automation there is no significant work. This paper deals with word-wise handwritten script identification for Indian postal automation. In the proposed scheme at first document skew is detected and corrected. Non-text parts are then segmented from the document using run length smoothing algorithm (RLSA). Next, using a piece-wise projection method the destination address block (DAB) is at first segmented into lines and then links into words. Using water reservoir concept we compute the busy-zone of the word. Finally, using matra/Shirorekha, water reservoir concept based feature, etc. a tree classifier is generated for word-wise Bangla/Devnagari and English scripts identification. <s> BIB012 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <s> Postal automation is a topic of research over the last few years. There are many works towards the postal automation in USA, UK, Japan and Australia, but for Indian postal automation there is no significant work. This paper deals with word-wise handwritten script identification for Indian postal automation. In the proposed scheme at first document skew is detected and corrected. Non-text parts are then segmented from the document using run length smoothing algorithm (RLSA). Next, using a piecewise projection method the destination address block (DAB), is at first segmented into lines and then into words. Using water reservoir concept we compute the busy-zone of the word. Finally, using matra/Shirorekha, water reservoir concept based feature, fractal based feature, etc. a neural network (NN) classifier is generated for word-wise Bangla and English scripts identification. Overall accuracy of the proposed system is at present 9 7.62%. <s> BIB013 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <s> Script identification prior to OCR is necessary in document image analysis. And each script has unique spatial distribution and visual attribute that make it possible to identify itself from other languages. The key technology of script identification algorithm is to abstract effective measure feature. By analyzing vision differences based on normalized histogram statistic, Chinese, Japanese, English and Russian are identified respectively from others. Therefore, automatic identification of four scripts is realized successfully. <s> BIB014 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <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> BIB015 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <s> In this paper, we present multilingual automatic identification of Arabic and Latin in both handwritten and printed script. The proposed scheme is based, Firstly, on morphological transform of line text images, secondly on fractal analysis features of both (i): original texture of 2-D images, (ii): vertical and horizontal profile projection. We used two techniques to obtain only 12 features based on fractal multi-dimension. The proposed system has been tested for 1000 prototypes with various typefaces, scriptors styles and sizes. The accuracy discrimination rate is about of 96.64 % by using KNN, and 98.72 % by using RBF. Experimental results show the importance of the proposed approach. <s> BIB016 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <s> In general, printed documents may contain several different languages. Therefore, to use Optical Character Recognition (OCR) for multi-lingual documents, it is necessary to automatically separate these languages. In this paper, we describe a method for identification of printed Farsi and English text from images of documents in line and word levels. The proposed algorithm is developed based on statistical and shape-based features. The accuracy of this method is around 96.05%. <s> BIB017 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <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> BIB018 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <s> Arabic and Latin language and script identification in machine printed and handwritten types present several difficulties because the Arabic (machine printed or handwritten) and the handwritten Latin scripts are cursive scripts of nature. To avoid all possible confusions which can be generated, we propose in this paper an accurate and suitable designed system for language and script identification at word level which is based on steerable pyramid transform. The features extracted from pyramid sub bands serve to classify the scripts on only one script among the scripts to identify. The encouraging and promising results obtained are presented in this research paper. <s> BIB019 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <s> A multilingual document may contain text words in more than one language. In a multilingual country like India it is necessary that a document should be composed of text contents in different languages in order to reach a larger cross section of people, But on the other hand, this causes practical difficulty in OCRing such a document, because the language type of the text should be pre-determined, before employing a particular OCR (Optical Character Recognition). It is perhaps impossible to design a single recognizer which can identify a large number of scripts/languages. So, it is necessary to identify the language region of the document before feeding the document to the corresponding OCR system. Script identification aims to extract information presented in digital documents namely articles, newspapers, magazines and e-books. This has given rise to many language identification systems. The objective of this paper is to propose a model to identify script type of different text portions using visual clues. In this work seven feature namely bottom max row, top horizontal lines, vertical lines, bottom components, tick components, top holes and bottom holes have been used to identify the script type. In this work, multilingual documents with Telugu, English and Hindi scripts have been used. From the experimentation it is understood that the identification accuracy of above 93% is achieved. <s> BIB020 </s> Script Identification of Multi-Script Documents: A Survey <s> 1) STATISTICAL FEATURES <s> Our ultimate objective is to contribute to the field of script and nature identification to be able to differentiate, at word level, handwritten or machine-printed, Arabic and Latin scripts. Different sets of features have been employed successfully for discriminating between Arabic and Latin words. They include few well-established features previously used and adapted in our case and new structural features which are intrinsic features of Arabic and Latin scripts. We select features that maximize the distinction between Arabic and Latin words. Experiments have been conducted with 1320 handwritten and printed words, covering a wide range of fonts, and encouraging results have been obtained. We achieved a correct classification of 98.4 percent for word level script and nature identification using Bayes classifier. <s> BIB021
Statistics-based features extract mathematical characteristics as the mean and variance of the width, height, ratio and area of the connected components. They concern methods that identify scripts through the analysis of the upward concavity, vertical and horizontal projections, etc. These methods are more suitable to scripts that differ significantly in style. Statistics-based approaches are highly sensitive to noise and image quality and all features are extracted at higher levels such as words, lines and text blocks. Some of the commonly used statistical features are BIB004 : Horizontal projection profiles BIB019 ,  , BIB006 , BIB014 , BIB020 , BIB008 , BIB002 , BIB004 , BIB007 - BIB010 , BIB009 , BIB011 , BIB018 , BIB021 ; Water reservoir-based features BIB016 , BIB015 ,  , BIB010 , BIB009 , BIB012 , BIB013 ; Bounding box feature BIB015 , BIB017 , BIB001 , BIB005 ; Character pitch features BIB015 ; Upward concavities  , BIB001 , BIB003 .