Multi-Oriented Gujarati Characters Recognition: A Review

Transcription

1 Multi-Oriented Gujarati Characters Recognition: A Review Nikisha B. Jariwala Asst. Professor Smt. Tanuben & Dr. Manubhai Trivedi College of Information Science, Surat, Gujarat, India nikisha_jariwala@yahoo.co.in Dr. Bankim Patel Director Shrimad Rajchandra Institute of Management & Computer Application, Gopal Vidyanagar, Bardoli, Gujarat, India bankin.patel@utu.ac.in Abstract In this paper, the concentration is done on the work carried out in various scripts along with their special properties, their feature extraction and recognition techniques along with the Multi-Oriented Characters Recognition. It also focuses on the problems related to the recognition of the Gujarati characters having different orientations. This paper would provide a pathway for developing recognition tools for Indian scripts and also locate the researchers the way where there is still a scope of recognition accuracy. Keywords Character recognition, Gujarati Text, Multi- Oriented, Pattern recognition I. INTRODUCTION In the era of technological development, Pattern recognition has become a very interesting topic for researchers from last few decades. Character recognition (Printed/Handwritten) is a Complex and challenging part of image and Pattern Recognition. Printed/typed characters are easy to recognize than Handwritten Characters. Printed characters have similar style and also have similar distance and inter word spacing. Efficient and accurate recognition of handwritten characters is difficult. The shape variation of handwritten characters causes the misclassification [1]. Most of the time, handwritten characters are not written in similar style. The distance and inter word spacing is also not similar of handwritten characters. Sometimes handwritten characters are joined with each other and become unreadable. Many researchers have carried out work to recognize these characters and many algorithms have been proposed to recognize characters. For more than 30+ years, researchers have been working on handwritten recognition [2]. Companies are also involved in the research of handwritten recognition from last few years. The characters can be recognized in two ways: online and offline [3]. In case of online character recognition, there is real time recognition of characters [4]. Online systems have better information for performing recognition and they avoid the initial search step of locating the character as in the case of offline character recognition. Online systems obtain the position of the pen as a function of time directly from the interface [2]. Use of paper to write handwritten text, converting it to an image using scanner, identifying handwritten characters from the image is known as off-line handwritten text recognition [5]. It is important to make digital copies of handwritten documents. Technology has advanced to tablet and many similar devices allow humans to input data in form of handwriting. Various applications for handwritten character recognition are available for bank cheques, converting historical documents into electronic form, automation of an administrative tasks etc. There are various factors involved in the character recognition from the document. The document is to be scanned, so the text is converted to image. Then the image is pre-processed, so the image is converted into editable format on the computer. The overview of some of the research work related to Gujarati character recognition is as follows: S. Antani et al. have worked on classification of printed or digitized Gujarati Characters. They have used K-NN and Euclidean minimum distance classifiers. The recognition rate achieved was 67% [6]. J. Dholakia et al. have provided an algorithm to identify various zone used for Gujarati Printed text. They have used horizontal and vertical profiles in an algorithm [7]. S. K. Shah et al. have used template matching and Fringe distance classifier as distance measure. They segmented printed characters in terms of lines, words and connected components. The overall result achieved was 72.3% [8]. J. Dholakia et al. used wavelet features, General Regression Neural Network (GRNN) and K-Nearest Neighbour (KNN) classifier on the printed Gujarati text. The experiment produces 97.59% and 96.71% as their respective recognition rates [9]. J. R. Prasad et al. have suggested Template Matching Technique for identifying Gujarati characters by analysing its shape and comparing its features that distinguish each character. The algorithm appears to be very robust against stroke order variations and large shape variations. The results seem encouraging [10].

2 M. Kayasth et al. used modified version of Hidden Markov Model (HMM) based algorithm and presented recognition of offline computer generated and printed Gujarati characters. They experimented with Nilkanth Gujarati font of the size 48 and 72 points. The results generated were fast and highly accurate. They got 100% success. They have also experimented with other fonts like Nil, GanSyam [11]. A. A. Desai utilized four profile vectors for feature extraction to identify digits. A feed forward back propagation neural network is used for Gujarati numeral classification. The result for standard fonts is 71.82%, for handwritten training sets is 91.0% while for testing sets is 81.5% [12]. A. A. Desai extended his work that mentioned in [12] by maintaining two aspects. One was the information of subdivision of the skeletonized image and second was the aspect ratio of the image before converting it into skeleton. Through k-nn classifier for classification of Gujarati handwritten numerals he achieved accuracy of 96.99% for training set and % for unseen data [13]. E. Hassan et al. used advancement in kernel methods for recognition of Gujarati text. They used concept of Multiple Kernel Learning (MKL) to improved results. The experiments have shown substantial improvement in results [14]. M. Maloo et al. propose the Support Vector Machine (SVM) based recognition scheme towards the recognition of Gujarati handwritten numerals. The pre-processing is done considering morphological operations. For computing the features, each isolated numeral is segmented into blocks. Then they derived affine invariant moments as features. The features obtained are fed to SVM classifier. They obtained the recognition rate of 91% approximately [15]. M.J. Baheti et al. made an attempt to compare the classifiers for offline handwritten character recognition system for the isolated Gujarati numerals. For feature extraction affine invariant moments based model is used. They used KNN classifier and Principal Component Analysis (PCA). KNN classifier yielded 90 % as recognition rate whereas PCA scored recognition rate of 84% [16]. M. Maloo et al. made an attempt to recognize the offline isolated handwritten Gujarati numerals. Affine invariant moments is used for extracting the features. The extracted feature set is treated with Principal Component Analysis (PCA), Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and Gaussian distribution function to classify the numerals. The comparison of all the classifiers is made and it can be seen that SVM classifier has shown better results as compared to PCA, KNN and Gaussian distribution function classifier [17]. A. H. Chokshi et al. used Fuzzy-KNN algorithm to recognize similar appearing Gujarati characters. They have also experimented with other classifiers such as KNN, General Regression Neural Network. They achieved almost 100% result with Fuzzy-KNN [18]. M. Chaudhary et al. have made an attempt to recognize similar looking printed Gujarati characters. They have introduced a mechanism called (Enhanced Supervised Locality Preserving Projections) ESLPP. The recognition accuracy in all the experiments is found to be very satisfactory [19]. B. Sojitra et al. used Neural Network for Gujarati Script Recognition. They conducted experiment for 10 data samples from 5 different fonts; fonts were selected in such a way that changes in curve of consonants of other fonts can be included in range of selected fonts. For optimum results, they use various parameters such as number of iteration, Learning Radius etc. of Self Organizing Map (SOM). The accuracy found was 97.78% and recognition rate for slight changes in training set was 98.83% [20]. M. J. Baheti et al. used Hybrid approach and Neural Network for identification of Gujarati numerals. For numerals 0 and 1, they have reported the recognition rate as 96% and 91% respectively. For numerals 3, 4, 5, 6, 7 and 8 less recognition rate is found to be 84%, 83%, 82%, 79%, 78% and 81%. Numeral 2 is found to procure recognition rate of 88% which is found to be much better as compared to others. Numerals 9 reported to procure very good recognition rate of 94%. The overall recognition rate is 86% [21]. S. Chaudhari et al. have described a system for recognition of offline multi-font computer generated and machine printed Gujarati numerals [22]. The overview of the research work found for other languages and Multi-Oriented characters domain: Y.Y. Chiang et al. have presented a text recognition approach that focuses on locating individual text labels in the map and detecting their orientations using morphological operator. Their approach detects accurate string orientations and achieves 96.2% precision and 94.7% recall on character recognition and 80.6% precision and 84.1% recall on word recognition [23]. Y.Y. Chiang et al. contribute toward the recognition of Multi-Oriented, Multi-Sized, and Curved English Text. Mainly they used Conditional Dilation Algorithm (CDA) for grouping Connected Component (CC) in the string. Their research can be easily integrated with commercial OCR product for text recognition from the document [24]. J. V. Beusekom et al. propose a one-step skew and orientation detection method for English Text using geometric text-line model. The method combines accurate skew estimation with resolution independent orientation detection. The effectiveness of the orientation detection approach is demonstrated on the UW-I dataset, and on publicly available test images. The method achieves an accuracy of 99% on the UW-I dataset and almost 100% on test images [25]. U. Pal et al. have worked on Multi-Oriented Lines Detection and their Skew Estimation. They used Component grouping technique for grouping the objects, Candidate Region Detection for detecting region of an object and Proposed Line Extraction Technique for detecting the baselines. They have experimented with 3045 text lines and they got 97.7% accuracy [26]. N. Das et al. utilized Novel Convex-Hull Based Alignment Technique for Isolated Multi-Oriented Handwritten /Printed Characters Recognition. They have worked on Devnagari and

3 Bangla text. They have taken different no. of sample for different languages. They got 91.75% accuracy for handwritten Bangla, 90% for Handwritten Devnagari and 98.42% for Printed Bangla [27]. P. Saragiotis et al. propose a technique for detecting and correcting the skew of English text areas in a document. They worked with the documents which may contain several areas of text with different skew angles. For each text area a local skew angle is estimated and then these text areas are skew corrected independently to horizontal or vertical orientation [28]. U. Pal et al. have proposed a contour distance-based recognition of multi-oriented and multi-sized isolated characters of printed script. They have tested on printed Bangla and Devnagari multi-oriented characters and obtained encouraging results [29]. G. Hemantha et al. have presented a technique to estimate skew angles of cone shaped text lines. The cone shaped text lines are corrected into single horizontal line using to different skew angles. The experimented result shows that method works satisfactorily [30]. N. Ouwayed et al. have presented an approach for the multi-oriented text line extraction from handwritten Arabic documents. They used the Wigner-Ville Distribution (WVD) to estimate the global orientation of the zone. It has been evaluated on 50 documents reaching an accuracy of about 97.6% [31]. N. Ouwayed et al. have extended their work mentioned in [31]. They used the Snake for line extraction. Once the paving is established, the orientation is determined using the Wigner- Ville distribution on the histogram projection profile. The proposed approach has been experimented on 100 documents reaching an accuracy of about 98.6% [32]. U. Pal et al. have used background and foreground information to recognize Bangla and Devnagari script. Convex hull and water reservoir principle have been applied for the cavity regions and background information of the document. Support Vector Machines (SVM) classifier was used for recognition. From the experiment they obtained recognition results of 99.18% when tested on 7515 Devnagari characters and 98.86% accuracy when tested on 7874 Bangla characters [33]. P. P. Roy et al. work on Multi-Oriented touching English characters recognition in Graphical Document. To detect touched characters they found big cavity region in background and used convex hull technique to handle information of background. Dynamic Programming is applied using total likelihood of characters as the objective function. They used SVM classifier. From the experiment, they obtained encouraging results [34]. There is no work found in Multi-Oriented Gujarati Character Recognition. II. PROBLEM India is multilingual country. So there are various scripts such as Hindi, Bangla, Kannada, Punjabi, Rajasthani, Malayalam, Marathi and Guajarati is also one of it. In Indian language there is no concept of upper case and lower case. Sometimes more than one characters are combined to form compound character. There are large character sets (vowels and consonants) in the Indian Languages. Gujarati script is derived from devnagari script [35]. The shapes of Gujarati characters are also very typical. Gujarati is descended from Sanskrit. There are over 50 million people worldwide who use Gujarati for writing and speaking. The earliest known document in Gujarati script is a manuscript dating from 1592, and the script first appeared in print in 1797 advertisement [36]. The Gujarati alphabet utilizes overall 75 distinct legitimate and recognized shapes, which mainly includes 59 Characters and 16 diacritics. Fifty-nine characters are divided into 36 consonants (34 Singular and 2 Compound (not lexically though)) means ornamented sounds, 13 vowels (pure sounds), and 10 numerical digits [12, 37]. Sixteen diacritics are divided into 13 vowel and 3 other characters. The alphabet is ordered by logically grouping the vowels and the consonants based on their pronunciations [11]. Gujarati is a phonetic language in western India. Gujarati script is written from left to right, with each character representing a syllable. The vowels are called Swar and consonants are called Vyanjan. Gujarati consist of set of special modifier symbols called Maatras, corresponding to each vowel, which are attached to consonants to change their sound. Modifiers are placed at the top, at bottom right or at bottom part of consonant. They are attached at different positions for different consonants. They can also occur in different shapes. A character is conjunct, if two half consonants are joined [20]. So, characters in Gujarati can be the combination of consonant, vowels and diacritics. Gujarati language has many characters as shown in Fig. 1. There are different ways to write in Gujarati language. If there are handwritten characters than again different people write with different styles which become difficult to recognize. Fig. 1 Gujarati Characters

4 Challenges in handwritten characters recognition lie in the variation and distortion of offline handwritten characters since different people may use different style of handwriting [2]. Some people write characters as slanted, curved, or diagonal. Such characters are called Multi-Oriented characters. Large amount of data is present in Multi-Oriented form, which needs to be recognized. Consider multi-oriented characters, they are written at any position and at any angle as shown in Fig. 2. It can also be written in horizontal or vertical manner. It requires much more efforts to recognize. Due to various types of fonts present, their curvedness also changes according to the font. If they are having different orientation then again they are difficult to recognize. To recognize the multi-oriented text, first it is to be converted into the straight / horizontal line. Then only it can be recognize. But if the characters are not clear and noise is present then again it becomes difficult to recognize the skew angle and the character. As shown in fig. 2 the text is written in curved shape, slant or in diagonal shape and in wave shape. Different methods need to be used to convert this text into straight line. Again this are typed text. If they are handwritten then the complexities related to the handwritten text will also be present in it. So it is difficult task to recognize Multi-Oriented text. Many applications such as Maps, advertisements, engineering drawings, class notes etc have multi-oriented characters which are difficult to recognize. It is important problem to be solved. The ultimate objective of any Handwritten Character Recognition system is to simulate the human reading capabilities so that the computer can read, understand, edit and do similar activities as human do with the text [1]. Fig. 2. Multi-Oriented Gujarati Text III. CONCLUSION The paper describes that moderate amount of work is done on Printed and Handwritten Gujarati Character Recognition. But still Multi-Oriented Gujarati characters recognition is untouch important problem. There are various methods, classifiers and algorithms available through which Multi- Oriented characters can be recognized from the document. In languages like English, Bangla, Kannada and Hindi, the work on Multi-Oriented Character Recognition is found, but no work is found in Gujarati language. So Multi-Oriented Printed and Handwritten Gujarati Character Recognition is the important problem to be solved. ACKNOWLEDGMENT I would like to express my sincere gratitude to Dr. Bankim Patel Director of Shrimad Rajchandra Institute of Management and Computer Application. His understanding, encouraging and guidance has provided fundamental basis for the paper. REFERENCES [1] D. Patel, T. Som, S. Yadav and M. Singh, Handwritten Character Recognition Using Multiresolution Technique and Euclidean Distance Metric, Journal of Signal and Information Processing, Vol 3, 2012, P.P [2] A. R. Vasant, S. R. Vasant and Dr. G. R. Kulkarni, Gujarati Character Recognition: The State of the Art Comprehensive Survey, Journal of Information, Knowledge and Research in Computer Engineering, Vol.2 Issue 1, Nov. 11 to Oct. 12, P.P [3] M. Maloo, Dr. K. V. Kale, Gujarati Script Recognition: A Review, International Journal of Computer Science Issues, Vol.8, Issue 4, No. 1, July 2011, P.P [4] CIA/DOE Partnership Program Proposal for FY99 (Sandia National Laboratories Proposal), [5] H. R. Thaker and C. K. Kumbharana, Study of Different Offline Handwritten Character Recognition Algorithms for Various Indian Scripts, International Journal of Computer Application, Vol. 65, No. 16, March 2013, P.P [6] S. Antani and L. Agnihotri, Gujarati Character Recognition, International Conference on Document Analysis and Recognition, Vol. 10, 1999, P.P [7] J. Dholakia, A. Negi and S. Ram Mohan, Zone Identification in the Printed Gujarati Text, International Conference on Document Analysis and Recognition, Vol. 1, Sep 2005, P.P [8] S. K. Shah and A. Sharma, Design Implementation of Optical Character Recognition System to Recognize Gujarati Script using Template Matching, IE (I) Journal-ET, Vol. 86, Jan 2006, P.P [9] J. Dholakia, A. Yajnik and A. Negi, Wavelet Feature Based Confision Character Sets for Gujarati Script, International Conference on Computational Intelligence and Multimedia Applications, Vol.2, Dec 2007, P.P [10] J. R. Prasad, U. V. Kulkarni and R. S. Prasad, Template Matching Technique for Gujarati Character Recognition, International Conference on Emerging Trends in Engineering and Technology, Dec. 2009, P.P [11] M. Kayasth and Dr. B. C. Patel, Offline Typed Gujarati Character Recognition, National Journal of System and Information Technology, Vol. 2, Issue 1, 2009, P.P [12] A. A. Desai, Gujarati Handwritten Numeral Optical Character Recognition through Neural Network, Pattern Recognition of Elsevier, Vol. 43, Issue 7, July 2010, P.P [13] A. A. Desai, Handwritten Gujarati Numeral Optical Character Recognition using Hybrid Feature Extraction Technique, Proceeding of International Conference on Image processing, Computer Vision and Pattern Recognition, Vol. 2, July 2010, P.P [14] E. Hassan, S. Chaudhury, M. Gopal, J. Dholakia, Use of MKL as Symbol Classifier for Gujarati Character Recognition, Proceeding of IAPR International workshop of Document Analysis Systems, 2010, P.P [15] M. Maloo and K. V. Kale, Support Vector Machine Based Gujarati Numeral Recognition, International Journal on Computer Science and Engineering, Vol. 3 No. 7, July 2011, P.P [16] M.J. Baheti, K.V.Kale, M.E. Jadav, Comparison of Classifiers for Gujarati Numeral Recognition, International Journal of Machine Intelligence, Vol. 3, Issue 3, 2011, P.P [17] M. Maloo and K. V. Kale, Gujarati Numeral Recognition: Affine Invariant Moments Approach, International Journal of electronics, Communication & Soft Computing Science & Engineering, March 2012, P.P

5 [18] A. H. Choksi and S. P. Thakkar, Recognition of Similar appearing Gujarati Characters using Fuzzy-KNN Algorithm, International Journal of Computer Applications, Vol. 55, No. 6, Oct 2012, P.P [19] M. Chaudhary, G. Shikkenawis, S. K. Mitra and M. Goswami, Similar Looking Gujarati Printed Character Recognition using Locality Preserving Projection and Artificial Neural Networks, International Conference on Emerging Applications and Information Technology, Dec. 2012, P.P [20] B. Sojitra and V. Dhakad, Neural Network in Character Recognition of Gujarati Script, Journal of Information, Knowledge and Research in Computer Engineering, Vol.2, Issue 2, Nov 12 to Oct 13, P.P [21] M.J. Baheti and K.V. Kale, Recognition of Gujarati Numerals using Hybrid Approach and Neural Networks, International Journal of Computer Applications, 2013, P.P [22] S. Chaudhari and G. M. Gulati, A Font Size Independent OCR for Machine Printed Gujarati Numerals, 3(1), P.P [23] Y. Y. Chiang and C. A. Knoblock, An Approach for Recognizing Text Labels in Raster Maps, International Conference on Pattern Recognition, 2010, P.P [24] Y.Y. Chiang, C.A. Knoblock, Recognition of Multi-Oriented, Multisized, and curved Text The ISI website [Online]. Available: [25] J. V. Beusekom, F. Shafait and T. M. Breuel, Combined Orientation and Skew Detection Using Geometric Text-Line Modeling, Available Online. [26] U. Pal, S. Shina, B. B. Chaudari, Multi-Oriented Text Lines Detection and their Skew Estimation The iitb website [Online]. Available: [27] N. Das, S. Pramanik, R. Sarkar, S. Basu, P. Kumar, Recognition of Isolated Multi-Oriented HandWritten/Printed characters using Novel Convex-Hull Based Alignment Technique, International Journal of Computer Applications, Vol.1, No.23, P.P [28] P. Saragiotis and N. Papamarkos, Local Skew Correction in Documents, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 22, No. 4, 2008, P.P [29] U. Pal and N. Tripathy, A Contour Distance-based approach for Multi-Oriented and Multi-Sized Character Recognition, Indian Academy of Sciences, Vol.34 Part 5, Oct 2009, P.P [30] G. Hemantha, P. Shivakumara, J. Vidyamba, H. S. Varsha, S. Rekha, M. R. Rashmi Nayaka, A New Skew Estimation Technique for Cone Shaped Text Line, available online. [31] N. Ouwayed and A. Belaid, Multi-Oriented Text Line Extraction from Handwritten Arabic Documents, Proceeding of International Workshop on Document Analysis Systems, 2008, P.P [32] N. Ouwayed, A. Belaid and F. Auger, General Text Line Extraction Approach based on Locally Orientation Estimation, Document Recognition and Retrieval Conference, [33] U. Pal, P. P. Roy, N. Tripathy and Josep Llados, Multi-Oriented Bangla and Devnagari Text Recognition, Pattern Recognition Journal Elsevier, Vol. 43, issue 12, Dec. 2010, P.P [34] P. P. Roy, U. Pal, J. Llados and M. Delalandre, Multi-Oriented touching Text Character Segmentation in Graphical Document using Dynamic Programming, Pattern Recognition Elsevier, Vol. 45, Issue 5, May 2012, P. P [35] M.J. Baheti, K.V.Kale, M.E. Jadav, Comparison of Classifiers for Gujarati Numeral Recognition, International Journal of Machine Intelligence, Vol. 3, Issue 3, 2011, P.P [36] Omniglot The Online encyclopedia of writing systems & Languages.[Online] Available: writing/ gujarati.htm. [37] Babu Suthar - Gujarati-English Learner s Dictionary.