Script Identification of Multi-Script Documents: A Survey <s> 2) STRUCTURE/GEOMETRIC FEATURES <s> This paper presents a new approach based on modified fractal signatures (MFS) and modified fractal features (MFF)for the discrimination of Oriental and Euramerican scripts. These methods will be useful in the measurement and classification of patterns. MFS do not need iterative breaking or merging, and can divide a document into blocks in a single step. MFF is also used in the identification and classification of a selected set of texture images with good results. It is anticipated that this approach could be widely used to process various types of documents, even including some with high geometrical complexity. <s> BIB001 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) STRUCTURE/GEOMETRIC 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> BIB002 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) STRUCTURE/GEOMETRIC FEATURES <s> In a multi-lingual country like India, a document page may contain more than one script form. Under the three-language formula, the document may be printed in English, Devnagari and one of the other Indian official languages. For Optical Character Recognition (OCR) of such a document page, it is necessary to separate these three script forms before feeding them to the OCRs of individual scripts. In this paper an automatic technique of separating the text lines is presented for almost all triplet of script forms. To do so, the triplets are grouped into five classes according to their characteristics, and shape based features have been employed to separate them without any expensive OCR-like algorithms. The proposed approaches are tested on many documents and the experimental results are presented. At present, the system has an overall accuracy of about 98.5%. <s> BIB003 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) STRUCTURE/GEOMETRIC FEATURES <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> BIB004 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) STRUCTURE/GEOMETRIC 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> BIB005 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) STRUCTURE/GEOMETRIC FEATURES <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> BIB006 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) STRUCTURE/GEOMETRIC FEATURES <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> BIB007 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) STRUCTURE/GEOMETRIC FEATURES <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> BIB008 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) STRUCTURE/GEOMETRIC 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> BIB009 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) STRUCTURE/GEOMETRIC FEATURES <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> BIB010 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) STRUCTURE/GEOMETRIC 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> BIB011 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) STRUCTURE/GEOMETRIC FEATURES <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> BIB012 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) STRUCTURE/GEOMETRIC 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> BIB013 </s> Script Identification of Multi-Script Documents: A Survey <s> 2) STRUCTURE/GEOMETRIC FEATURES <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> BIB014
Structural features include loops, cusps, endpoints, starts points, etc.. Structural features depend on the instinctive aspects of writing and are based on the geometric appearance of scripts. Some typical structural features are BIB004 ,  , BIB002 , BIB005 : Head-line (heuristics) features  , BIB013 , BIB003 , BIB002 , BIB005 , BIB012 ; Fractal-based features BIB009 , BIB014 , BIB001 ; Topological features BIB010 , BIB011 , BIB012 ; Morphological features  ,  , BIB006 , BIB007 , BIB008 ,  .