Script Identification of Multi-Script Documents: A Survey <s> 4) CHARACTER LEVEL SCRIPT IDENTIFICATION <s> This paper deals with an Optical Character Recognitionsystem for printed Urdu, a popular Indian script. Thedevelopment of OCR for this script is difficult because (i) alarge number of characters have to be recognized (ii) thereare many similar shaped characters. In the proposedsystem individual characters are recognized using acombination of topological, contour and water reservoirconcept based features. The feature detection methods aresimple and robust. A prototype of the system has beentested on printed Urdu characters and currently achieves97.8% character level accuracy on average. <s> BIB001 </s> Script Identification of Multi-Script Documents: A Survey <s> 4) CHARACTER LEVEL SCRIPT IDENTIFICATION <s> India is a multi-lingual multi-script country but there is not much work towards handwritten character recognition of Indian languages. In this paper we propose a modified quadratic classifier based scheme towards the recognition of off-line handwritten numerals of six popular Indian scripts. Here we consider Devnagari, Bangla, Telugu, Oriya, Kannada and Tamil scripts for our experiment. The features used in the classifier are obtained from the directional information of the numerals. For feature computation, the bounding box of a numeral is segmented into blocks and the directional features are computed in each of the blocks. These blocks are then down sampled by a Gaussian filter and the features obtained from the down sampled blocks are fed to a modified quadratic classifier for recognition. Here we have used two sets of feature. We have used 64 dimensional features for high-speed recognition and 400 dimensional features for high-accuracy recognition in our proposed system. A five-fold cross validation technique has been used for result computation and we obtained 99.56%, 98.99%, 99.37%, 98.40%, 98.71% and 98.51% accuracy from Devnagari, Bangla, Telugu, Oriya, Kannada, and Tamil scripts, respectively. <s> BIB002 </s> Script Identification of Multi-Script Documents: A Survey <s> 4) CHARACTER LEVEL SCRIPT IDENTIFICATION <s> This paper reports a statistical script identification technique that determines the script of document images, especially camera-based images which suffer from perspective distortion. The identification technique represents a document image by a frequency vector of affine invariant signatures of characters, and identifies the script by comparing the vector with pre-prepared script templates. Experimental results show that our method is tolerant to moderate perspectives, document skew and various image noises. <s> BIB003 </s> Script Identification of Multi-Script Documents: A Survey <s> 4) CHARACTER LEVEL SCRIPT IDENTIFICATION <s> Online character recognition of Urdu script based languages is one of the most difficult task due to cursive in nature. Urdu script based languages has not received much attestation even this script is used 1/6th of the population. The complexities in the script makes more complicated the recognition process. The problem in handwritten numeral recognition is the shape similarity between handwritten numerals. This paper presents a fuzzy rule base approach for the recognition of numerals both Urdu and Arabic in unconstrained environment. The proposed system is tested and provides accuracy of 96.3%. <s> BIB004 </s> Script Identification of Multi-Script Documents: A Survey <s> 4) CHARACTER LEVEL SCRIPT IDENTIFICATION <s> Character recognition problems of distinct scripts have their own script specific characteristics. The state-of-art optical character recognition systems use different methodolgies, to recognize different script characters, which are most effective for the corresponding script. The identificaton of the script of the individual character has not brought much attention between researchers, most of the script identification work is on document, line and word level. In this multilingual/multiscript world presence of different script characters in a single document is very common. We here propose a system to encounter such adverse situation in context of English and Gurumukhi Script. Experiments on multifont and multisized characters with Gabor features based on directional frequency and Gradient features based on gradient information of an individual character to identify it as Gurumukhi or English and also as character or numeral are reported here. Treating it as four class classification problem, multi-class Support Vector Machine(One Vs One) has been used for classification. We got promising results with both types of features. The average identification rates obtained with Gabor and Gradient features are 98.9% and 99.45% respectively. <s> BIB005
Pal and Sarkar BIB001 used a combination of topological, contour and water reservoir concept based features to identify printed Urdu script. Rani et al. BIB005 carried out experiments on multi-font and multi-sized characters with Gabor features and Gradient features to identify Gurumukhi and English scripts at character or numeral level. Pal et al. BIB002 proposed a modified quadratic classifier based scheme for the recognition of off-line handwritten numerals of six popular Indian scripts: Devnagari, Bangla, Telugu, Oriya, Kannada and Tamil. Razzak et al. BIB004 presented a fuzzy rule based approach for the recognition of both Urdu and Arabic numerals in an unconstrained environment. Li and Tan BIB003 reported a script identification based on statistical features technique to identify character level English, Arabic and Chinese scripts of camera-based images. The experimental results show that this method is tolerant to moderate perspective variations and document skew.