Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <s> Abstract This paper presents a survey of the literature on automatic signature verification and writer identification by computer, and an overview of achievements in static and dynamic approaches to solving these problems, with a special focus on preprocessing techniques, feature extraction methods, comparison processes and performance evaluation. In addition, for each type of approache special attention is given to requirement analysis, human factors, practical application environments, and appropriate definitions and terminology. Throughout the paper, new research directions are suggested. <s> BIB001 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <s> In a multi-lingual country like India, a document may contain more than one script forms. For such a document it is necessary to separate different script forms before feeding them to OCRs of individual script. In this paper an automatic word segmentation approach is described which can separate Roman, Bangla and Devnagari scripts present in a single document. The approach has a tree structure where at first Roman script words are separated using the 'headline' feature. The headline is common in Bangla and Devnagari but absent in Roman. Next, Bangla and Devnagari words are separated using some finer characteristics of the character set although recognition of individual character is avoided. At present, the system has an overall accuracy of 96.09%. <s> BIB002 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <s> When scanning documents with a large number of pagessuch as books, it is often feasible to provide a minimalnumber of training samples to personalize the system tocompensate for global shifts in how the document wascreated or in scanning parameters. In this paper, wepresent a supervised multi-class classifier based onGabor filters that is used to classify the scripts, font-faces,and font-styles (bold, italic, normal etc.) in anapplication where the classes are known. Classificationis performed at the word level (glyphs separated by whitespace) given training samples of each class. This methodwas applied to a variety of bilingual dictionaries toidentify different scripts, and simultaneously, to classifyRoman scripts into bold, italic and normal font-styles.Experimental results show the effectiveness of thisapproach in increasing performance over classifierstrained for general documents. <s> BIB003 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <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> BIB004 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <s> Reasonable success has been achieved at developing monolingual OCR systems in Indian scripts. Scientists, optimistically, have started to look beyond. Development of bilingual OCR systems and OCR systems with capability to identify the text areas are some of the pointers to future activities in Indian scenario. The separation of text and non-text regions before considering the document image for OCR is an important task. In this paper, we present a biologically inspired, multi-channel filtering scheme for page layout analysis. The same scheme has been used for script recognition as well. Parameter tuning is mostly done heuristically. It has also been seen to be computationally viable for commercial OCR system development. <s> BIB005 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <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> BIB006 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <s> This paper aims at the script identification problem of handwritten document images, which facilitates many important applications such as sorting, transcription of multilingual documents and indexing of large collection of such images, or as a precursor to optical character recognition (OCR). The script identification scheme proposed in this paper has two phases. First phase reports the script identification of text words using global and local features, extracted by morphological filters and regional descriptors of three major Indian languages/scripts: Kannada, Roman and Devnagari. In the second phase Kannada and Roman handwritten numerals script identification is carried out. For classification of text words and numerals, a K nearest neighbour algorithm is used. The proposed algorithm achieves an average maximum recognition accuracy is 96.05% and 99% respectively for text words and numerals with five fold cross validation test. The data set containing 3000 text words and 400 numerals collected from 250 writers. The novelty of the proposed algorithm is robust for noise, writer style, size and ink etc. <s> BIB007 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL 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 a few significant works. This paper deals with tri-lingual word-wise handwritten script identification for Indian postal automation. In the proposed scheme using Run Length Smoothing Algorithm, postal document is segmented into lines and then into words. Using fractal-based features, busy-zone based features and topological features, a neural network classifier is used for word-wise Bangla, English and Devnagari scripts identification. Overall accuracy of the proposed system is at present 96.79%. <s> BIB008 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <s> Feature extraction is one of the basic function of handwritten script identification. It involves measuring those features of the input pattern are relevant to classification. This paper provides a review of these advances. The aim is to provide an appreciation for the range of techniques that have been developed rather than to simply list sources. Various types of features proposed for handwritten script identification include horizontal and vertical histogram, curvature information and local extreme of curvature, topological features such as loops is a group of white pixels surrounded by black ones, end points is point with exactly 1 neighbouring point, dots a cluster of say 1-3 pixels and junction is a point with more than 2 neighbours all in thinned black and white images, Parameters of polynomial or curve fitting functions, contour information where contours is the outside boundary of a pattern. <s> BIB009 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <s> Script identification has always been a topic of much research interest in the field of document analysis. The accurate determination of the identity of the script is paramount to many post-processing steps such as document sorting, translation and in determining the choice of linguistic resources to use for OCR or handwriting recognition. However, few works exist with regards to the identification of online handwritten scripts, partly due to the large variations and challenges innate in handwritten scripts. This paper proposes a novel approach for online handwritten script identification based on the Information Retrieval model. We attempt to identify among three script families; Arabic, Roman and Tamil scripts, which attained an average accuracy of 93.3% from our results. This signifies promising potential in utilizing Information Retrieval models for script identification. <s> BIB010 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <s> In this paper a Bilingual Character Recognition System is proposed for the characterization and classification of printed Malayalam-English characters. Indian scripts in general are rich in patterns and variations. Gabor features are extracted after the word level segmentation to identify the script and recognition is based on characterization using Dominant singular values. A recognition rate of 96.5% was achieved for the two–stage classification approach. <s> BIB011 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <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> BIB012 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <s> Arabic and Latin script identification in printed and handwritten nature present several difficulties because the Arabic (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 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> BIB013 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <s> All Han-based scripts (Chinese, Japanese, and Korean) possess similar visual characteristics. Hence system development for identification of Chinese, Japanese and Korean scripts from a single document page is quite challenging. It is noted that a Han-based document page might also have Roman script in them. A multi-script OCR system dealing with Chinese, Japanese, Korean, and Roman scripts, demands identification of scripts before execution of respective OCR modules. We propose a system to address this problem using directional features along with a Gaussian Kernel-based Support Vector Machine. We got promising results of 98.39% script identification accuracy at character level and 99.85% at block level, when no rejection was considered. <s> BIB014 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <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> BIB015 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <s> Script identification is very important part to develop the scripts OCR systems. In this paper, we proposed a new algorithm for script identification based on the global and local texture of document images. The bidimensional empirical mode decomposition (BEMD) method is used to decompose the image to some components (IMFs) and then the Local Binary Patterns (LBP) method is used to detect the features. Experiments shown the recognition rate based on BEMD-LBP is as well as the LBPV and wavelet based energy feature in 0 angles. At the same time, for the different angles rotation script, the BEMD-LBP feature present some robust adaptive to the rotation script. <s> BIB016 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <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> BIB017 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL 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> BIB018 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <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> BIB019 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <s> Automated systems for understanding low resolution images of display boards are facilitating several new applications such as blind assistants, tour guide systems, location aware systems and many more. Script identification at character/word level is one of the very important pre-processing steps for development of such systems prior to further image analysis. In this paper, a new fuzzy based approach for word level script identification of text in low resolution images of display boards is presented. The proposed methodology uses horizontal run statistics and wavelet features for distinguishing 5 Indian scripts namely; Hindi, Kannada, English, Malyalam and Tamil. The method works in two phases; In the first phase, the wavelet transform based texture features such as zone wise wavelet energy features, vertical run statistical features of wavelet coefficients and wavelet log mean deviation features of decomposed energy bands at 2 levels are obtained from training word images and crisp sets are constructed, one for each script/language under study. The second phase is testing, in which test word image is processed to obtain horizontal run statistics to determine whether it belongs to Hindi script. Otherwise, the word image is processed to obtain a crisp vector. The degree of belongingness of crisp vector with each candidate object in the crisp sets is determined using newly devised fuzzy membership function. Further, fuzzy inference scheme is used to identify the script of the test word image. The proposed method is robust and insensitive to the variations in size and style of font, number of characters, thickness and spacing between characters, noise, and other degradations. The proposed method achieves an overall script identification accuracy of 94.33% and individual identification accuracy of 100% for Hindi Script, 98.67% for Kannada Script, 100% for English, 89% for Malyalam and 84% for Tamil Script. <s> BIB020 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <s> Character recognition problems of distinct scripts have their own script specific characteristics. The state-of-art optical character recognition systems use different methodolgies, to recognize different script characters, which are most effective for the corresponding script. The identificaton of the script of the individual character has not brought much attention between researchers, most of the script identification work is on document, line and word level. In this multilingual/multiscript world presence of different script characters in a single document is very common. We here propose a system to encounter such adverse situation in context of English and Gurumukhi Script. Experiments on multifont and multisized characters with Gabor features based on directional frequency and Gradient features based on gradient information of an individual character to identify it as Gurumukhi or English and also as character or numeral are reported here. Treating it as four class classification problem, multi-class Support Vector Machine(One Vs One) has been used for classification. We got promising results with both types of features. The average identification rates obtained with Gabor and Gradient features are 98.9% and 99.45% respectively. <s> BIB021 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <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> BIB022 </s> Script Identification of Multi-Script Documents: A Survey <s> B. GLOBAL FEATURES <s> In a multi-script country like India script identification from document images is an essential step before choosing appropriate script specific OCR (Optical Character Recognizer). The problem of handwritten script identification is more challenging compared to printed one due to uneven variations with respect to writers, time, content etc. Increasing efforts are coming day by day from document image processing researchers to develop standard techniques for Indic script identification. But most of the works is found to be considering printed script document images. In this paper a simple, robust and segmentation free technique based on different image transform methods and statistical features to identify any one of the four popular Indic scripts namely Bangla, Roman, Devanagari and Oriya is proposed. A dataset of total 101 handwritten document images comprising of more than 11000 words and 1300 lines with almost equal distribution of each type of scripts are built, which were collected from different writers with varying age, sex and educational qualification. On experimentation, an average accuracy rate of 88.1% is found for Four-scripts combination by MLP (Multilayer Perceptron) classifier after five fold cross validation. The average Tri-Scripts and Bi-Scripts accuracy are found to be 89.7% and 94.3% respectively. The outcome of this work is really impressive considering inherent complexities of handwritten Indic scripts. <s> BIB023
Global features for script identification are based on DCT, DWT, Gabor, steerable pyramids, and Radon transform BIB014 ,  , BIB019 , BIB002 , BIB006 , BIB016 , BIB001 , BIB008 , BIB010 . These are robust to noise, small skew, and faster in computation than local features. In general, global features are considered to be efficient in characterizing large size texture patterns, e.g. text blocks. Furthermore, since these features regard a text block as one single entity, analysis at the levels of text lines, words or connected components is not possible BIB009 BIB007 . Typical global features are derived from texture analysis approaches and include Gabor Filter BIB003 , BIB023 , BIB005 ,  , BIB017 , BIB011 ,  , Wavelet Transform features BIB020 , BIB015 , BIB004 , Discrete Cosine Transform (DCT)  , Gray level co-occurrence matrix BIB012 , BIB015 , rotation invariant features  , gradient features  , BIB021 , BIB022 ,  , steerable pyramid transforms BIB013 , BIB018 , etc..