Script Identification of Multi-Script Documents: A Survey <s> A. LOCAL 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> BIB001 </s> Script Identification of Multi-Script Documents: A Survey <s> A. LOCAL 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> BIB002 </s> Script Identification of Multi-Script Documents: A Survey <s> A. LOCAL FEATURES <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> BIB003
The analysis of local features mainly considers the analysis of intrinsic features such as character shape based features, structural features, statistical features, morphological, topological and contour based features, water reservoir principle VOLUME 5, 2017 based features, etc. The extraction of these features is time consuming, but they convey relevant characteristics for script identification BIB002 , BIB001 ,  , BIB003 :