Script Identification of Multi-Script Documents: A Survey <s> 3) WORD LEVEL SCRIPT IDENTIFICATION <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> BIB001 </s> Script Identification of Multi-Script Documents: A Survey <s> 3) WORD LEVEL 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> BIB002 </s> Script Identification of Multi-Script Documents: A Survey <s> 3) WORD LEVEL 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> BIB003 </s> Script Identification of Multi-Script Documents: A Survey <s> 3) WORD LEVEL SCRIPT IDENTIFICATION <s> In a multi-lingual country like India, english has proven to be the binding language. So, a line of a bilingual document page may contain text words in regional language and numerals in English (printed or handwritten). For optical character recognition (OCR) of such a document page, it is necessary to identify different script forms before running an individual OCR of the scripts. In this paper an automatic technique for script identification at word level based on morphological reconstruction is proposed for two printed bilingual documents of Kannada and Devnagari containing English numerals (printed and handwritten). The technique developed includes a feature extractor and a classifier. The feature extractor consists of two stages. In the first stage, shape (eccentricity, aspect ratio) and directional stroke features (horizontal and vertical) are extracted based on morphological erosion and opening by reconstruction using the line structuring element. The average height of all the connected components of an image is used to threshold the length of the structuring element. In the second stage, average pixel distribution is obtained from these resulting images. The k-nearest neighbour algorithm is used to classify the new word images. The proposed algorithm is tested on 2250 sample words with various font styles and sizes. The results obtained are quite encouraging. <s> BIB004 </s> Script Identification of Multi-Script Documents: A Survey <s> 3) WORD LEVEL 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> BIB005 </s> Script Identification of Multi-Script Documents: A Survey <s> 3) WORD LEVEL SCRIPT IDENTIFICATION <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> BIB006 </s> Script Identification of Multi-Script Documents: A Survey <s> 3) WORD LEVEL 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> BIB007 </s> Script Identification of Multi-Script Documents: A Survey <s> 3) WORD LEVEL 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 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> 3) WORD LEVEL SCRIPT IDENTIFICATION <s> India is a multi-lingual country where Roman script is often used alongside different Indic scripts in a text document. To develop a script specific handwritten Optical Character Recognition (OCR) system, it is therefore necessary to identify the scripts of handwritten text correctly. In this paper, we present a system, which automatically separates the scripts of handwritten words from a document, written in Bangla or Devanagri mixed with Roman scripts. In this script separation technique, we first, extract the text lines and words from document pages using a script independent Neighboring Component Analysis technique (1). Then we have designed a Multi Layer Perceptron (MLP) based classifier for script separation, trained with 8 different word- level holistic features. Two equal sized datasets, one with Bangla and Roman scripts and the other with Devanagri and Roman scripts, are prepared for the system evaluation. On respective independent text samples, word-level script identification accuracies of 99.29% and 98.43% are achieved. <s> BIB009 </s> Script Identification of Multi-Script Documents: A Survey <s> 3) WORD LEVEL 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> BIB010 </s> Script Identification of Multi-Script Documents: A Survey <s> 3) WORD LEVEL SCRIPT IDENTIFICATION <s> Every country has their own language and script. This may or may not common to other countries. To communicate with each other we need to have a common language. English is the language that is performing that role. So most of the countries (other than Roman) use bi-script documents. But for countries like India where we have a total of 12 official scripts (and 22 languages) things are more complex. So to have an OCR we need to identify the script by which the script the document is written (even the document is not itself multi-script). Postal document, pre-printed forms are good example of such documents. So identification of the script from a document may be written with any of these 13 scripts is a very challenging work. In this paper we have tried to identify scripts written by any of the 6 official languages of India. Here we have used very simple and efficient feature at component level for the same. Using Fractal-based features, component based feature and Topological features, series of classifiers were used. Overall accuracy of the proposed system is at present 89.48% on the test set without rejection. <s> BIB011 </s> Script Identification of Multi-Script Documents: A Survey <s> 3) WORD LEVEL SCRIPT IDENTIFICATION <s> For a multi script/lingual country like India Script identification is a complex real life problem for automation of document processing. Handwritten script identification is again much more complex compared to print one. Here scripts from multi script handwritten documents are identified and then performance is compared using different well known classifiers. We followed a two stage approach for the same. Firstly, we have identified six scripts used for writing six official languages of India in Handwritten domain, which are easily available to us. Using some Abstract/Mathematical features, Structure based features and Script dependent features at document level a 41 dimensional feature set is prepared. Then, a series of classifiers namely Logistic Model Tree, Random Forest, Multi Layer Perceptron, Sequential Minimal Optimization, LibLINEAR, RBFNetwork and Fuzzy Unordered Rule Induction Algorithm are applied on the feature set to classify among the six handwritten scripts and the results are compared. Among all these classifiers, Logistic Model Tree shows highest accuracy rate of 91.2% with a 5 fold cross validation whereas SMO model has lowest convergence time of 0.05s. <s> BIB012 </s> Script Identification of Multi-Script Documents: A Survey <s> 3) WORD LEVEL 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> BIB013 </s> Script Identification of Multi-Script Documents: A Survey <s> 3) WORD LEVEL SCRIPT IDENTIFICATION <s> The presence of multiple scripts in multi-lingual document images makes Optical Character Recognition (OCR) of such documents a challenging task. Due to the unavailability of a single OCR system which can handle multiple scripts, script identification becomes an essential step for choosing the appropriate OCR. Although, there are various techniques available for script identification from handwritten and printed documents having simple backgrounds, however script identification from video frames has been seldom explored. Video frames are coloured and suffer from low resolution, blur, complex background and noise to mention a few, which makes the script identification process a challenging task. This paper presents a study of various combinations of features and classifiers to explore whether the traditional script identification techniques can be applied to video frames. A texture based feature namely, Local Binary Pattern (LBP), Gradient based features namely, Histogram of Oriented Gradient (HoG) and Gradient Local Auto-Correlation (GLAC) were used in the study. Combination of the features with SVMs and ANNs where used for classification. Three popular scripts, namely English, Bengali and Hindi were considered in the present study. Due to the inherent problems with the video, a super resolution technique was applied as a pre-processing step. Experiments show that the GLAC feature has performed better than the other features, and an accuracy of 94.25% was achieved when testing on 1271 words from three different scripts. The study also reveals that gradient features are more suitable for script identification than the texture features when using traditional script identification techniques on video frames. <s> BIB014 </s> Script Identification of Multi-Script Documents: A Survey <s> 3) WORD LEVEL SCRIPT IDENTIFICATION <s> Script identification at the word level is challenging because of complex backgrounds and low resolution of video. The presence of graphics and scene text in video makes the problem more challenging. In this paper, we employ gradient angle segmentation on words from video text lines. This paper presents new Gradient-Angular-Features (GAF) for video script identification, namely, Arabic, Chinese, English, Japanese, Korean and Tamil. This work enables us to select an appropriate OCR when the frame has words of multi-scripts. We employ gradient directional features for segmenting words from video text lines. For each segmented word, we study the gradient information in effective ways to identify text candidates. The skeleton of the text candidates is analyzed to identify Potential Text Candidates (PTC) by filtering out unwanted text candidates. We propose novel GAF for the PTC to study the structure of the components in the form of cursiveness and softness. The histogram operation on the GAF is performed in different ways to obtain discriminative features. The method is evaluated on 760 words of six scripts having low contrast, complex background, different font sizes, etc. in terms of the classification rate and is compared with an existing method to show the effectiveness of the method. We achieve 88.2% average classification rate. <s> BIB015 </s> Script Identification of Multi-Script Documents: A Survey <s> 3) WORD LEVEL SCRIPT IDENTIFICATION <s> Use of multiple scripts for information communication through various media is quite common in a multilingual country. Optical character recognition of such document images or videos assists in indexing them for effective information retrieval. Hence, script identification from multi-lingual documents/images is a necessary step for selecting the appropriate OCR, due the absence of a single OCR system capable of handling multiple scripts. Script identification from printed as well as handwritten documents is a well-researched area, but script identification from video frames has not been explored much. Low resolution, blur, noisy background, to mention a few are the major bottle necks when processing video frames, and makes script identification from video images a challenging task. This paper examines the potential of Bag-of-Visual Words based techniques for word-wise script identification from video frames. Two different approaches namely, Bag-Of-Features (BoF) and Spatial Pyramid Matching (SPM), using patch based SIFT descriptors were considered for the current study. SVM Classifier was used for analysing the three popular south Indian scripts, namely Tamil, Telugu and Kannada in combination with English and Hindi. A comparative study of Bag-of-Visual words with traditional script identification techniques involving gradient based features (e.g. HoG) and texture based features (e.g. LBP) is presented. Experimental results shows that patch-based features along with SPM outperformed the traditional techniques and promising accuracies were achieved on 2534 words from the five scripts. The study reveals that patch-based feature can be used for scripts identification in-order to overcome the inherent problems with video frames. <s> BIB016
Dhanya and Ramakrishnan BIB001 presented a successful method for identifying script at word level in a bilingual document containing Roman and Tamil scripts. Jaeger et al. BIB002 used a Gabor filter analysis of textures and a multiple classifier system with four different classifiers to identify Arabic, Chinese, Hindi, and Korean scripts at word-level. Dhandra et al.  , BIB004 proposed an automatic technique for script identification at word level based on the morphological reconstruction of two printed scripts: Telugu and Devnagari. A SVM based method was proposed by Chanda et al. BIB005 for the identification of printed English and Thai scripts at word level from a single line of a document page. Chanda et al. BIB007 proposed a SVM based technique for word-level identification of Sinhala, Tamil and English scripts from a single document page, and a SVM based scheme for the identification of printed word-level English, Devnagari and Bangla scripts  . Roy et al. BIB003 proposed a word-wise handwritten script identification method for Indian postal automation regarding Bangla and English script identification at wordlevel. The method mainly uses water reservoir concept based features, fractal-based features and a Neural Network classifier. Roy and Majumder BIB008 also developed a technique for script separation of handwritten postal documents in Bangla, Roman and Devanagri scripts. Run Length Smoothing Algorithm (RLSA) was used to segment the document pages into lines and then into words. Fractalbased, busy-zone and topological features were used along with a Neural Network (NN) classifier for script identification. A script separation technique of Roman and Oriya scripts for Indian Postal automation was proposed by Zhou et al.  . They presented a script identification method based on water reservoir concept based features, fractal dimension based features and topological features with an NN classifier. Sarkar et al. BIB009 presented an automatic separation system for word-level script identification from Bangla or Devanagri mixed with Roman scripts. Dhandra and Hangarge BIB006 used a two-stage approach. In the first stage, some global and local features were applied to identify the text words. In the second stage, the numeral written in different scripts was identified. To test the system, Kanada, Devanagri and Roman scripted handwritten documents were considered. A word-wise handwritten script identification system for bi-script documents written in Persian and Roman scripts was proposed by Roy et al. BIB010 . The system used a simple and fast computable sets of twelve features based on fractal dimension, position of small components and topology. A scheme for document level handwritten script identification from six popular Indian script documents was presented by Roy et al. BIB011 . In the proposed scheme, a small set of features based also on fractal dimension are computed using an MLP classifier. Obaidullah et al. BIB012 proposed a scheme to identify the six popular Bangla, Devnagari, Malayalam, Urdu, Oriya and Roman scripts in Indian documents, and compared performance using different well-known classifiers. Sharma et al. BIB013 used Zernike moments, Gabor and gradient features with SVM classifiers to identify English, Bengali and Hindi scripts. A study of word level multi script identification from video frames is proposed by Sharma et. al. BIB014 using different combinations of texture based features namely, Local Binary Pattern (LBP), Gradient, Histogram of Oriented Gradient (HoG) and Gradient Local Auto-Correlation (GLAC) features. SVMs and ANNs classifiers were applied for English, Bengali and Hindi scripts identification. This experiment pointed out the efficiency of gradient features for low resolution, blur, complex background, and noise video based images. Shivakumara et. al BIB015 developed a word level script identification method for Arabic, Chinese, English, Japanese, Korean and Tamil scripts by using new Gradient Angular Features. Bag-of-Visual Words based word-wise script identification from video images is presented by Sharma et al. BIB016 for five different south Indian scripts.