Script Identification of Multi-Script Documents: A Survey <s> C. SCRIPT IDENTIFICATION IN HYBRID DOCUMENTS <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> BIB001 </s> Script Identification of Multi-Script Documents: A Survey <s> C. SCRIPT IDENTIFICATION IN HYBRID DOCUMENTS <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> BIB002 </s> Script Identification of Multi-Script Documents: A Survey <s> C. SCRIPT IDENTIFICATION IN HYBRID DOCUMENTS <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> BIB003 </s> Script Identification of Multi-Script Documents: A Survey <s> C. SCRIPT IDENTIFICATION IN HYBRID DOCUMENTS <s> Our ultimate objective is to contribute to the field of script and nature identification to be able to differentiate, at word level, handwritten or machine-printed, Arabic and Latin scripts. Different sets of features have been employed successfully for discriminating between Arabic and Latin words. They include few well-established features previously used and adapted in our case and new structural features which are intrinsic features of Arabic and Latin scripts. We select features that maximize the distinction between Arabic and Latin words. Experiments have been conducted with 1320 handwritten and printed words, covering a wide range of fonts, and encouraging results have been obtained. We achieved a correct classification of 98.4 percent for word level script and nature identification using Bayes classifier. <s> BIB004
Hybrid documents include printed and handwritten texts. A multi-lingual automatic identification of Arabic and Latin in both handwritten and printed script was proposed by Ben Moussa et al BIB002 . A method for Arabic and Latin text block differentiation for both printed and handwritten scripts is discussed by Kanoun et al. BIB001 . The method is based on a morphological analysis for each script at the text-block level and a geometrical analysis at line and connected component levels. Benjelil et al. BIB003 proposed an accurate system based on a steerable pyramid transform for Arabic and Latin script identification at word level. By using new structural features, a successful attempt was made by Saidani et al. BIB004 to identify the Arabic or Latin script of a machine printed or handwritten document at word level.