Script Identification of Multi-Script Documents: A Survey <s> VI. PERFORMANCE ANALYSIS <s> We describe an automated script identification system for typeset document images. Templates for each script are created by clustering textual symbols from a training set. Symbols from new images are compared to the templates to find the best script. Our current system processes thirteen scripts with minimal preprocessing and high accuracy. <s> BIB001 </s> Script Identification of Multi-Script Documents: A Survey <s> VI. PERFORMANCE ANALYSIS <s> In this paper, the study of script identification based on morphological reconstruction for printed document images is carried out. The system is developed by using 609-scanned document images representing English, Hindi, Kannada, and Urdu scripts. The system developed includes a feature extractor and a classifier. The feature extractor consists of two stages. In the first stage, the morphological erosion and opening by reconstruction is carried out on a document image in horizontal, vertical, right and left diagonal directions using the line structuring element. The length of the structuring element is fixed, based on the average height of all the connected components of an image. In the next stage, average pixel distribution is found in these resulting images. A nearest neighbor analysis is used to classify the new documents. Accuracy of classification averaged 97% across the four scripts. The method shows robustness with respect to noise, font sizes and styles. <s> BIB002 </s> Script Identification of Multi-Script Documents: A Survey <s> VI. PERFORMANCE ANALYSIS <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> BIB003 </s> Script Identification of Multi-Script Documents: A Survey <s> VI. PERFORMANCE ANALYSIS <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> BIB004 </s> Script Identification of Multi-Script Documents: A Survey <s> VI. PERFORMANCE ANALYSIS <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> BIB005 </s> Script Identification of Multi-Script Documents: A Survey <s> VI. PERFORMANCE ANALYSIS <s> Document Script Identification (DSI) is a very useful application in document processing. This paper presents a method for this application that uses a new noise tolerant feature, the Downgraded Pixel Density feature. Compared to other features widely used in existing DSI solutions, this new feature is much more robust to variations in slant, font and style of printed documents. Experimental results show that the method achieves promising identification performances. <s> BIB006 </s> Script Identification of Multi-Script Documents: A Survey <s> VI. PERFORMANCE ANALYSIS <s> We present a method for spotting words in the wild, i.e., in real images taken in unconstrained environments. Text found in the wild has a surprising range of difficulty. At one end of the spectrum, Optical Character Recognition (OCR) applied to scanned pages of well formatted printed text is one of the most successful applications of computer vision to date. At the other extreme lie visual CAPTCHAs - text that is constructed explicitly to fool computer vision algorithms. Both tasks involve recognizing text, yet one is nearly solved while the other remains extremely challenging. In this work, we argue that the appearance of words in the wild spans this range of difficulties and propose a new word recognition approach based on state-of-the-art methods from generic object recognition, in which we consider object categories to be the words themselves. We compare performance of leading OCR engines - one open source and one proprietary - with our new approach on the ICDAR Robust Reading data set and a new word spotting data set we introduce in this paper: the Street View Text data set. We show improvements of up to 16% on the data sets, demonstrating the feasibility of a new approach to a seemingly old problem. <s> BIB007 </s> Script Identification of Multi-Script Documents: A Survey <s> VI. PERFORMANCE ANALYSIS <s> In this paper, we propose a method for classifying textual entities of bilingual documents written in Chinese and English. In contrast to earlier works that performed classification on the level of text lines or documents, we apply our method to the level of textual components, as we must first identify Chinese components before merging them into intact characters and sending the latter characters to a Chinese recognizer. To cope with a large training data set containing 365,672 samples, we employ a decision-tree support vector machine (DTSVM) method, which decomposes a given data space into small regions and trains local SVMs on those regions. By applying this method to train classifiers on various combinations of feature types, we were able to complete each training process within 3,500 seconds and achieve higher than 99.6% test accuracy in classifying a textual component into Chinese, alphanumeric, and punctuation. Moreover, the classification had no strong bias towards any of the three categories. <s> BIB008 </s> Script Identification of Multi-Script Documents: A Survey <s> VI. PERFORMANCE ANALYSIS <s> Recognition of text in natural scene images is becoming a prominent research area due to the widespread availablity of imaging devices in low-cost consumer products like mobile phones. To evaluate the performance of recent algorithms in detecting and recognizing text from complex images, the ICDAR 2011 Robust Reading Competition was organized. Challenge 2 of the competition dealt specifically with detecting/recognizing text in natural scene images. This paper presents an overview of the approaches that the participants used, the evaluation measure, and the dataset used in the Challenge 2 of the contest. We also report the performance of all participating methods for text localization and word recognition tasks and compare their results using standard methods of area precision/recall and edit distance. <s> BIB009 </s> Script Identification of Multi-Script Documents: A Survey <s> VI. PERFORMANCE ANALYSIS <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> BIB010 </s> Script Identification of Multi-Script Documents: A Survey <s> VI. PERFORMANCE ANALYSIS <s> With the rapid increase of transnational communication and cooperation, people frequently encounter multilingual scenarios in various situations. In this paper, we are concerned with a relatively new problem: script identification at word or line levels in natural scenes. A large-scale dataset with a great quantity of natural images and 10 types of widely-used languages is constructed and released. In allusion to the challenges in script identification in real-world scenarios, a deep learning based algorithm is proposed. The experiments on the proposed dataset demonstrate that our algorithm achieves superior performance, compared with conventional image classification or script identification methods, including as the original CNN architecture, LLC and GLCM. <s> BIB011
Performance of some of the most significant systems presented in the recent literature are reported in Table 6 . More precisely, Table 6 shows that different kinds of features, e.g., statistical features, structural features, symbols matching features and texture features, were generally used for script identification. Indeed, these features express only some characteristics of scripts that are generally not sufficient for script identification. Conversely, texture features are generally more efficient than others, but they cannot be applied reliably at word and character level within a document. Furthermore, to achieve better results, different kinds of features and classifiers were used. Experimental results show that a particular feature, that is generally efficient within a set of scripts, is not necessarily useful for other scripts. In English, Kannada, Hindi multi-script documents BIB002 ,  ,  , texture features with a K-NN classifier were more useful than other features. For identifying different scripts in Indian documents, whatever classifier was used, both Global and Local features demonstrated to be efficient. Moreover, results obtained using a single feature (either local or global) were generally worse than those obtained using both features BIB003 , VOLUME 5, 2017 [83]. In Han, Roman and some alphabet multi script identification methods, an SVM classifier with different extracted features achieved higher identification results than K-NN and NN classifiers BIB005 , BIB008 , BIB006 . For Arabic and Latin scripts, an RBF classifier was more efficient than a K-NN classifier BIB004 . It is worth noting that approaches in Table6 are difficult to be compared since performance have been estimated using databases collected in laboratory environments. In fact, although there existed several public datasets containing natural images with texts, they are mainly related to the text recognition task BIB009 , BIB007 . Some datasets specifically devoted to script identification have been also realized BIB001 , BIB010 ,  . Among the others, the SIW-10 dataset was developed for script identification BIB011 . The SIW-10 dataset contains more than 13,000 multi-scripts images including textual components from 10 languages. Therefore, to date, it can be considered as one of the most valuable benchmarking dataset for research in script recognition.