Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <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> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> This paper presents efficient algorithms for determining the language classification of machine generated documents without requiring the identification of individual characters. Such algorithms may be useful for sorting and routing of facsimile documents as they arrive so that appropriate routing and secondary analysis, which may include OCR, is selected for each document. It may also prove useful as a component of a content addressable document access system. There have been numerous reported efforts which attempt to segment printed documents into homogeneous regions using Hough transforms, hidden Markov models, morphological filtering, and neural networks. However, language identification can be accomplished without explicit segmentation using the less computationally intensive methods described. <s> BIB002 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR 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> V. CLASSIFICATION TECHNIQUES FOR 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> BIB004 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR 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> BIB005 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <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> BIB006 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> A method for Arabic and Latin text block differentiation for printed and handwritten scripts is proposed. This method is based on a morphological analysis for each script at the text block level and a geometrical analysis at the line and the connected component level. In this paper, we present a brief survey, of existing methods used for scripts differentiation as well as a general characteristics of Arabic and Latin scripts. Then, We describe our method for the differentiation of these last scripts. We finally show two experimental results on two different data sets. 400 text blocks constitute the first one and 335 text blocks compose the second. <s> BIB007 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> Current approaches to script identification rely onhand-selected features and often require processing a significantpart of the document to achieve reliable identification.We present an approach that applies a large pool ofimage features to a small training sample and uses subsetfeature selection techniques to automatically select a subsetwith the most discriminating power. At run time we usea classifier coupled with an evidence accumulation engineto report a script label once a preset likelihood thresholdhas been reached. We apply the system to a diverse corpusof printed Russian and English documents that suffer fromcommon degradation problems. Our validation studyshows promising results both in terms of the script identificationaccuracy and the ability to identify script on thescale of individual words and text lines. <s> BIB008 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR 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> BIB009 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <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> BIB010 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> The problem of determining the script and language of a document image has a number of important applications in the field of document analysis, such as indexing and sorting of large collections of such images, or as a precursor to optical character recognition (OCR). In this paper, we investigate the use of texture as a tool for determining the script of a document image, based on the observation that text has a distinct visual texture. An experimental evaluation of a number of commonly used texture features is conducted on a newly created script database, providing a qualitative measure of which features are most appropriate for this task. Strategies for improving classification results in situations with limited training data and multiple font types are also proposed. <s> BIB011 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> In this paper, we present a multiple classifier system for script identification. Applying a Gabor filter analysis of textures on word-level, our system identifies Latin and non-Latin words in bilingual printed documents. The classifier system comprises four different architectures based on nearest neighbors, weighted Euclidean distances, Gaussian mixture models, and support vector machines. We report results for Arabic, Chinese, Hindi, and Korean script. Moreover, we show that combining informational confidence values using sum-rule can consistently outperform the best single recognition rate. <s> BIB012 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> India isamulti-lingual andmulti-script country wherealineofabilingual document pagemaycontain text wordsinregional language andnumerals inEnglish. ForOptical Character Recognition (OCR)ofsucha document page, itisnecessary toidentify different script formsbefore running anindividual OCR ofthescripts. In thispaper, weexamine theuseofdiscriminating features (aspect ratio, strokes, eccentricity, etc,) asa toolfor determining thescript atwordlevel inthreebilingual documents representing Kannada, TamilandDevnagari containing English numerals, basedontheobservation thatevery texthasthedistinct visual appearance. Thek- nearest neighbour algorithm isusedtoclassify thenew wordimages. Theproposed algorithm istested on2500 samplewordswithvarious fontstyles andsizes. The results obtained arequite encouraging. <s> BIB013 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> In this paper we employ appearance based models for document script identification. They are employed to identify scripts at both paragraph and word level. Elaborate experimentation has been conducted which has revealed that they are robust enough to handle highly confusing scripts and their performance does not degrade drastically even in the presence of noise. A generic script identification has been attempted, to identify both Asian and European scripts by considering a dataset of twenty different languages. <s> BIB014 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> This paper presents an identification technique that automatically detects the underlying script and orientation of scanned document images. In the proposed technique, document script and orientation are identified by using the stroke density and distribution, which convert each document image into a document vector. For each script at each orientation, a number of reference document vectors are first constructed. Script and orientation of the query document are then determined according to the similarity between the query document vector and multiple pre- constructed reference document vectors by using the K-nearest neighbor algorithm. Experiments show that the proposed technique is tolerant to the document skew and able to detect orientations of documents of different scripts. <s> BIB015 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> In some Thai documents, a single text line of a document page may contain both Thai and English scripts. For the optical character recognition (OCR) of such a document page it is better to identify, at first, Thai and English script portions and then to use individual OCR system of the respective scripts on these identified portions. In this paper, a SVM based method is proposed for identification of word-wise printed English and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of the individual character group combining different character features obtained from structural shape, profile, component overlapping information, topological properties, water reservoir concept etc. Based on the experiment on 6110 data we obtained 99.36% script identification accuracy from the proposed scheme. <s> BIB016 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> India is a multi-lingual multi-script country but there is not much work towards handwritten character recognition of Indian languages. In this paper we propose a modified quadratic classifier based scheme towards the recognition of off-line handwritten numerals of six popular Indian scripts. Here we consider Devnagari, Bangla, Telugu, Oriya, Kannada and Tamil scripts for our experiment. The features used in the classifier are obtained from the directional information of the numerals. For feature computation, the bounding box of a numeral is segmented into blocks and the directional features are computed in each of the blocks. These blocks are then down sampled by a Gaussian filter and the features obtained from the down sampled blocks are fed to a modified quadratic classifier for recognition. Here we have used two sets of feature. We have used 64 dimensional features for high-speed recognition and 400 dimensional features for high-accuracy recognition in our proposed system. A five-fold cross validation technique has been used for result computation and we obtained 99.56%, 98.99%, 99.37%, 98.40%, 98.71% and 98.51% accuracy from Devnagari, Bangla, Telugu, Oriya, Kannada, and Tamil scripts, respectively. <s> BIB017 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> There are many documents in Srilanka where a single document page may contain Sinhala, Tamil and English texts. For OCR development of such a document page, it is better to identify different scripts present in the page and thenfeed the identifiedportion to the respective OCR module. In this paper, a SVM based technique is proposed for word-wise identification of Sinhala, Tamil and English scripts from a single document page. Structural features, topological features and water reservoir principle basedfeatures are mainly used here for the purpose. From the experiment we obtainedencouragingresults. <s> BIB018 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> We report an algorithm to identify the script of each word in a document image. We start with a bi-script scenario which is later extended to tri-script and then to eleven-script scenarios. A database of 20,000 words of different font styles and sizes has been collected and used for each script. Effectiveness of Gabor and discrete cosine transform (DCT) features has been independently evaluated using nearest neighbor, linear discriminant and support vector machines (SVM) classifiers. The combination of Gabor features with nearest neighbor or SVM classifier shows promising results; i.e., over 98% for bi-script and tri-script cases and above 89% for the eleven-script scenario. <s> BIB019 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> This article primarily concerns the problem of isolated handwritten numeral recognition of major Indian scripts. The principal contributions presented here are (a) pioneering development of two databases for handwritten numerals of two most popular Indian scripts, (b) a multistage cascaded recognition scheme using wavelet based multiresolution representations and multilayer perceptron classifiers and (c) application of (b) for the recognition of mixed handwritten numerals of three Indian scripts Devanagari, Bangla and English. The present databases include respectively 22,556 and 23,392 handwritten isolated numeral samples of Devanagari and Bangla collected from real-life situations and these can be made available free of cost to researchers of other academic Institutions. In the proposed scheme, a numeral is subjected to three multilayer perceptron classifiers corresponding to three coarse-to-fine resolution levels in a cascaded manner. If rejection occurred even at the highest resolution, another multilayer perceptron is used as the final attempt to recognize the input numeral by combining the outputs of three classifiers of the previous stages. This scheme has been extended to the situation when the script of a document is not known a priori or the numerals written on a document belong to different scripts. Handwritten numerals in mixed scripts are frequently found in Indian postal mails and table-form documents. <s> BIB020 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> Online character recognition of Urdu script based languages is one of the most difficult task due to cursive in nature. Urdu script based languages has not received much attestation even this script is used 1/6th of the population. The complexities in the script makes more complicated the recognition process. The problem in handwritten numeral recognition is the shape similarity between handwritten numerals. This paper presents a fuzzy rule base approach for the recognition of numerals both Urdu and Arabic in unconstrained environment. The proposed system is tested and provides accuracy of 96.3%. <s> BIB021 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> In this paper, we propose a novel texture feature extraction method which compute the weighted moment of the wavelet energy histogram of the decomposed images. A total of 15 features are extracted for script classification. The author choose six languages (Arabia, Chinese, English, Hindi, Thailand and Korean) to demonstrate the potential of the technology. Experimental results show that the propose method has less number of features and is more effective in script identification than the traditional wavelet energy and ratio method and histogram signatures algorithm. <s> BIB022 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR 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> BIB023 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> Most of the countries use bi-script documents. This is because every country uses its own national language and English as second/foreign language. Therefore, bi-lingual document with one language being the English and other being the national language is very common. Postal documents are a very good example of such bi-lingual/script document. This paper deals with word-wise handwritten script identification from bi-script documents written in Persian and Roman. In the proposed scheme, simple but fast computable set of 12 features based on fractal dimension, position of small component, topology etc. are used and a set of classifiers are employed for script identification experiments. We tested our scheme on a dataset of 5000 handwritten Persian and English words and 99.20% of correct script identification is obtained. <s> BIB024 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> In this paper, we propose a method for classifying textual entities of bilingual documents written in Chinese and English. In contrast to earlier works that performed classification on the level of text lines or documents, we apply our method to the level of textual components, as we must first identify Chinese components before merging them into intact characters and sending the latter characters to a Chinese recognizer. To cope with a large training data set containing 365,672 samples, we employ a decision-tree support vector machine (DTSVM) method, which decomposes a given data space into small regions and trains local SVMs on those regions. By applying this method to train classifiers on various combinations of feature types, we were able to complete each training process within 3,500 seconds and achieve higher than 99.6% test accuracy in classifying a textual component into Chinese, alphanumeric, and punctuation. Moreover, the classification had no strong bias towards any of the three categories. <s> BIB025 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> This paper addresses the script identification problem of bilingual printed document images. We propose an OCR system that separates and identify mixed English-Gujarati digits. Here, first the system is trained with standard data samples. Then for testing, data samples are collected from different sources of paper like, news paper, book, magazine, etc. Random sized pre-processed image is normalized to uniform sized image. A statistical approach is used for feature extraction. For classification kNN classifier is used. The model gives average accuracy of 99.26% for Gujarati digits, 99.20% for English digits, and overall accuracy 99.23%. <s> BIB026 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> Script identification is an essential step for the efficient use of the appropriate OCR in multilingual document images. There are various techniques available for script identification from printed and handwritten document images, but script identification from video frames has not been explored much. This paper presents a study of some pre-processing techniques and features for word-wise script identification from video frames. Traditional features, namely Zernike moments, Gabor and gradient, have performed well for handwritten and printed documents having simple backgrounds and adequate resolution for OCR. Video frames are mostly coloured and suffer from low resolution, blur, background noise, to mention a few. In this paper, an attempt has been made to explore whether the traditional script identification techniques can be useful in video frames. Three feature extraction techniques, namely Zernike moments, Gabor and gradient features, and SVM classifiers were considered for analyzing three popular scripts, namely English, Bengali and Hindi. Some pre-processing techniques such as super resolution and skeletonization of the original word images were used in order to overcome the inherent problems with video. Experiments show that the super resolution technique with gradient features has performed well, and an accuracy of 87.5% was achieved when testing on 896 words from three different scripts. The study also reveals that the use of proper pre-processing approaches can be helpful in applying traditional script identification techniques to video frames. <s> BIB027 </s> Script Identification of Multi-Script Documents: A Survey <s> V. CLASSIFICATION TECHNIQUES FOR SCRIPT IDENTIFICATION <s> This paper presents directional discrete cosine transform (D-DCT) based word level handwritten script identification. The conventional discrete cosine transform (DCT)emphasizes vertical and horizontal energies of an image and de-emphasizes directional edge information, which of course plays a significant role in shape analysis problem, in particular. Conventional DCT however, is not efficient in characterizing the images where directional edges are dominant. In this paper, we investigate two different methods to capture directional edge information, one by performing 1D-DCT along left and right diagonals of an image, and another by decomposing 2D-DCT coefficients in left and right diagonals. The mean and standard deviations of left and right diagonals of DCT coefficients are computed and are used for the classification of words using linear discriminant analysis (LDA) and K-nearest neighbour (K-NN). We validate the method over 9000 words belonging to six different scripts. The classification of words is performed at bi-scripts, triscripts and multi-scripts scenarios and accomplished the identification accuracies respectively as 96.95%, 96.42% and 85.77% in average. <s> BIB028
Although classification is a crucial step of script identification systems, the literature shows that only a few simple classifiers were used in earlier works, as Table 5 reports  , BIB013 , BIB003 , BIB004 , BIB001 , BIB005 , BIB002 . The K-Nearest Neighbor (K-NN) classifier has been extensively used in script identification systems based on Gabor filter  ,  , Cartesian moments BIB008 , appearance based model approaches BIB014 , grey level co-occurrence matrix features  , statistical-based features BIB026 , stroke density and distribution-based features BIB015 , texture features BIB026 , BIB009 . Support vector machine (SVM) has also been applied to script identification. SVM-based systems for script identification use structural features, topological features and water reservoir principle based features BIB016 , BIB018 , Zernike momentbased feature  , BIB027 , Gabor, and gradient features BIB027 . Other classification methods were considered for script identification such as Neural Network BIB020 , BIB010 , quadratic classifier BIB028 ,  , BIB017 , BIB006 , BIB022 , rule-based classifiers BIB008 , BIB023 ,  , BIB021 , Linear Discriminant Classifiers BIB028 , BIB025 , BIB019 , Gaussian Mixture Model BIB011 , BIB012 , BIB007 , BIB024 , etc..