[1. Broumandnia, A., J. Shanbehzadeh. Fast Zernike Wavelet Moments for Farsi Character Recognition. - Image and Vision Computing, Vol. 25, 2007, 717-726.10.1016/j.imavis.2006.05.014]Search in Google Scholar
[2. Morita, M. E., E. Lethelier, A. E. Yacoubi, F. Bortolozzi, R. Sabourin, P. Pontifícia. Recognition of Handwritten Dates on Bankchecks Using an HMM Approach. - In: 13th Brazilian Symposium on Computer Graphics and Image Processing, Gramado, Brazil, 2000.]Search in Google Scholar
[3. Pan, X., X. Ye, S. Zhang. A Hybrid Method for Robust Car Plate Character Recognition. -Engineering Applications of Artificial Intelligence, Vol. 18, 2005, No 8, 963-972.10.1016/j.engappai.2005.03.011]Search in Google Scholar
[4. AI-A l a w i, R. A Hybrid w-Tuple Neuro-Fuzzy Classifier for Handwritten Numerals Recognition. - In: IEEE International Joint Conference on Neural Networks, Budapest, Hungary, 2004.]Search in Google Scholar
[5. Amin, A. Recognition of Printed Arabic Text Based on Global Features and Decision Tree Learning Techniques. - Pattern Recognition, Vol. 33, 2000, No 8, 1309-1323.10.1016/S0031-3203(99)00114-4]Search in Google Scholar
[6. N a g ab hushan, P., R. M. P a i. Modified Region Decomposition Method and Optimal Depth Decision Tree in the Recognition of Non-Uniform Sized Characters - An Experimentation with Kannada Characters. - Pattern Recognition Letters, Vol. 20, 1999, No 14, 1467-1475.10.1016/S0167-8655(99)00058-6]Search in Google Scholar
[7. C h e o k, A. D., Z. J i a n, E. S. C h n g. Efficient Mobile Phone Chinese Optical Character Recognition Systems by Use of Heuristic Fuzzy Rules and Bigram Markov Language Models. - Applied Soft Computing, Vol. 8, 2008, No 2, 1005-1017.10.1016/j.asoc.2007.02.013]Search in Google Scholar
[8. Chi, Z., M. Suters, H. Yan. Handwritten Digit Recognition Using Combined ID3-Derived Fuzzy Rules and Markov Chains. - Pattern Recognition, Vol. 29, 1996, No 11, 1821-1833.10.1016/0031-3203(96)00040-4]Search in Google Scholar
[9. Hanmandlua, M., K. R. M. Mohanb, S. Chakrabortyc, S. Goyald. Unconstrained Handwritten Character Recognition Based on Fuzzy Logic. - Pattern Recognition, Vol. 36, 2003, 603-623.10.1016/S0031-3203(02)00069-9]Search in Google Scholar
[10. Patil, P. M., T. R. Sontakke. Rotation, Scale and Translation Invariant Handwritten Devanagari Numeral Character Recognition Using General Fuzzy Neural Network. - Pattern Recognition, Vol. 40, 2007, No 7, 2110-2117.10.1016/j.patcog.2006.12.018]Search in Google Scholar
[11. Goltsev, A., D. Rachkovskij. Combination of the Assembly Neural Network with a Perceptron for Recognition of Handwritten Digits Arranged in Numeral Strings. - Pattern Recognition, Vol. 38, 2005, No 3, 315-322.10.1016/j.patcog.2004.09.001]Search in Google Scholar
[12. Lauer, F., C. Y. Suen, G. Bloch. A Trainable Feature Extractor for Handwritten Digit Recognition. - Pattern Recognition, Vol. 40, 2007, No 6, 1816-1824.10.1016/j.patcog.2006.10.011]Search in Google Scholar
[13. Camastra, F. A SVM-Based Cursive Character Recognizer. - Pattern Recognition, Vol. 40, 2007, No 12, 3721-3727.10.1016/j.patcog.2007.03.014]Search in Google Scholar
[14. Malon, C., S. Uchida, M. Suzuki. Mathematical Symbol Recognition with Support Vector Machines. - Pattern Recognition Letters, Vol. 29, 2008, No 9, 1326-1332.10.1016/j.patrec.2008.02.005]Search in Google Scholar
[15. Amor, N. B., N. E. B. A mara. Multifont Arabic Character Recognition Using Hough Transform and Hidden Markov Models. - In: Fourth International Symposium on Image and Signal Processing and Analysis, Zagreb, Croatia, 2005.10.1109/ISPA.2005.195424]Search in Google Scholar
[16. Bunke, H., M. Roth, E. Schukat-Talamazzini. Off-Line Cursive Handwriting Recognition Using Hidden Markov Models. - Pattern Recognition, Vol. 28, 1995, No 1, 399-1413.10.1016/0031-3203(95)00013-P]Search in Google Scholar
[17. Khorsheed, M. S. Recognising Handwritten Arabic Manuscripts Using a Single Hidden Markov Model. - Pattern Recognition Letters, Vol. 24, 2003, No 14, 2235-2242.10.1016/S0167-8655(03)00050-3]Search in Google Scholar
[18. Theeramunkong, T., C. Wongtapan. Off-Line Isolated Handwritten Thai OCR Using Island-Based Projection with w-Gram Model and Hidden Markov Models. - Information Processing and Management, Vol. 41, 2005, No 1, 139-160.10.1016/j.ipm.2004.04.011]Search in Google Scholar
[19. Nagabhushan, P., R. M. Pai. Modified Region Decomposition Method and Optimal Depth Decision Tree in the Recognition of Non-Uniform Sized Characters - An Experimentation with Kannada Characters. - Pattern Recognition Letters, Vol. 20, 1999, No 14, 1467-1475.10.1016/S0167-8655(99)00058-6]Search in Google Scholar
[20. Ping, Z., C. Lihui. A Novel Feature Extraction Method and Hybrid Tree Classification for Handwritten Numeral Recognition. - Pattern Recognition Letters, Vol. 23, 2002, No 1-3, 4556.10.1016/S0167-8655(01)00088-5]Search in Google Scholar
[21. Oliveira, L. S., F. Bortolozzi, C. Y. Suen. Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy. - IEEE Transactions on Pattern Recognition and Machine Intelligence, Vol. 24, 2001, No 11, 1448-1456.10.1109/TPAMI.2002.1046154]Search in Google Scholar
[22. Mohiuddin, K. M., J. Mao. A Comprehensive Study of Different Classifiers for HandPrinted Character Recognition. - Pattern Recognition, Practice IV, 1994, 437-448.10.1016/B978-0-444-81892-8.50043-2]Search in Google Scholar
[23. Koerich, A. L. Unconstrained Handwritten Character Recognition Using Different Classification Strategies. - In: International Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), 2003.]Search in Google Scholar
[24. Kim, H., K. K. Kim, C. Y. Suen. Hybrid Schemes of Homogeneous and Heterogeneous Classifiers for Cursive Word Recognition. - In: Proceedings of Seventh International Workshop on Frontiers in Handwritten Recognition, Amsterdam, 2000, 433-442.]Search in Google Scholar
[25. Al-Omari, F. A., O. Al-Jarrah. Handwritten Indian Numerals Recognition System Using Probabilistic Neural Networks. - Advanced Engineering Informatics, Vol. 18, 2004, No 1, 916.10.1016/j.aei.2004.02.001]Search in Google Scholar
[26. Pal, U., B. B. Chaudhuri. Indian Script Character Recognition: A Survey. - Pattern Recognition, Vol. 37, 2004, No 9, 1887-1899.10.1016/j.patcog.2004.02.003]Search in Google Scholar
[27. Hung, K.-Y., R. W. P. L uk, D. S. Yeung, K. F. L. Chung, W. Shu. A Multiple Classifier Approach to Detect Chinese Character Recognition Errors. - Pattern Recognition, Vol. 38, 2005, No 5, 723-738.10.1016/j.patcog.2004.09.004]Search in Google Scholar
[28. Li, Y.-X., C. L. Tan, X. Ding, C. Liu. Contextual Post-Processing Based on the Confusion Matrix in Offline Handwritten Chinese Script Recognition. - Pattern Recognition, Vol. 37, 2004, No 9, 1901-1912.10.1016/j.patcog.2004.03.002]Search in Google Scholar
[29. Su, T.-H., T.-W. Zhang, D.-J. Guan, H.-J. Huang. Off-Line Recognition of Realistic Chinese Handwriting Using Segmentation-Free Strategy. - Pattern Recognition, Vol. 42, 2009, No 1, 167-182.10.1016/j.patcog.2008.05.012]Search in Google Scholar
[30. Zhao, S., Z. Chi, P. Shi, H. Yan. Two-Stage Segmentation of Unconstrained Handwritten Chinese Characters. - Pattern Recognition, Vol. 36, 2003, No 1, 145-156.10.1016/S0031-3203(02)00041-9]Search in Google Scholar
[31. Itoh, N. Japanese Language Model Based on Bigrams and Its Application to On-Line Character Recognition. - Pattern Recognition, Vol. 28, 1995, No 2, 135-141.10.1016/0031-3203(94)E0053-N]Search in Google Scholar
[32. Kimura, F., T. Wakabayashi, S. Tsuruoka, Y. Miyake. Improvement of Handwritten Japanese Character Recognition Using Weighted Direction Code Histogram. -Pattern Recognition, Vol. 30, 1997, No 8, 1329-1337.10.1016/S0031-3203(96)00153-7]Search in Google Scholar
[33. Srihari, S. N., T. Hong, G. Srikantan. Machine-Printed Japanese Document Recognition. - Pattern Recognition, Vol. 30, 1997, No 8, 1301-1313.10.1016/S0031-3203(96)00168-9]Search in Google Scholar
[34. Al-Muhtaseb, H. A., S. A. Mahmo ud, R. S. Qahwaj i. Recognition of Off-Line Printed Arabic Text Using Hidden Markov Models. - Signal Processing, Vol. 88, 2008, No 12, 2902-2912.10.1016/j.sigpro.2008.06.013]Search in Google Scholar
[35. Azmi, R., E. Kabir. A New Segmentation Technique for Omnifont Farsi Text. - Pattern Recognition Letters, Vol. 22, 2001, 97-104.10.1016/S0167-8655(00)00086-6]Search in Google Scholar
[36. Dehghan, M., K. Faez, M. Ahmadi, M. Shridha r. Unconstrained Farsi Handwritten Word Recognition Using Fuzzy Vector Quantization and Hidden Markov Models. - Pattern Recognition Letters, 2001, 209-214.10.1016/S0167-8655(00)00090-8]Search in Google Scholar
[37. Mozaffari, S., K. Faez, V. Märgner, H. El-Abed. Lexicon Reduction Using Dots for Off-Line Farsi/Arabic Handwritten Word Recognition. - Pattern Recognition Letters, Vol. 29, 2008, No 6, 724-734.10.1016/j.patrec.2007.11.009]Search in Google Scholar
[38. Solimanpour, F., J. Sadri, C. Y. Suen. Standard Databases for Recognition of Handwritten Digits, Numerical Strings, Legal Amounts, Letters and Dates in Farsi Language. - In: 10th Int'l Workshop on Frontiers of Handwriting Recognition, La Baule, France, 2006.]Search in Google Scholar
[39. Ziaratban, M., K. Faez, F. Faradji. Language-Based Feature Extraction Using Template-Matching in Farsi/Arabic Handwritten Numeral Recognition. - In: 9th Int'l Conference on Document Analysis and Recognition, Curitiba, Brazil, 2007.10.1109/ICDAR.2007.4405576]Search in Google Scholar
[40. B lumen stein, M., X. Y. Liu, B. Verm a. A Modified Direction Feature for Cursive Character Recognition. - In: IEEE International Joint Conference on Neural Networks, Vol. 4, 2000, 2983-2987.]Search in Google Scholar
[41. Fitzgerald, J. A., F. Geiselbrechtinger, T. Kechadi. Application of Fuzzy Logic to Online Recognition of Handwritten Symbols. - In: Proceedings of Ninth International Workshop on Frontiers in Handwritten Recognition, 2004, 395-400.]Search in Google Scholar
[42. Fletcher, R., C. M. Reeves. Function Minimization by Conjugate Gradients. - Computer Journal, Vol. 7, 2004, 149-154.10.1093/comjnl/7.2.149]Search in Google Scholar
[43. Hagan, M. T., H. B. Demuth, M. H. Be ale. Neural Network Design. Boston, MA, PWS Publishing, 1996.]Search in Google Scholar
[44. Powell, M. J. D. Restart Procedures for the Conjugate Gradient Method. - Mathematical Programming, Vol. 12, 1977, 241-254.10.1007/BF01593790]Search in Google Scholar
[45. Beale, E. M. L. A Derivation of Conjugate Gradients. - In: F. A. Lootsma, Ed. Numerical Methods for Nonlinear Optimization. London, Academic Press, 1972.]Search in Google Scholar
[46. Mo l l e r, M. F. A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning. - Neural Networks, Vol. 6, 1993, 525-533.10.1016/S0893-6080(05)80056-5]Search in Google Scholar
[47. Battiti, R. First and Second Order Methods for Learning: Between Steepest Descent and Newton's Method. - Neural Computation, Vol. 2, 1992, No 4, 141-166.10.1162/neco.1992.4.2.141]Search in Google Scholar
[48. Sexton, A. P., V. Sorge. Database-Driven Mathematical Character Recognition, Graphics Recognition, Algorithms and Applications (GREC). - In: Lecture Notes in Computer Science (LNCS), Hong Kong, 2006, 206-217.10.1007/11767978_20]Search in Google Scholar
[49. Riedmiller, M., H. Braun. A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm. - In: Proceedings of IEEE International Conference on Neural Networks, 1993.]Search in Google Scholar
[50. De Campos, T. E., B. R. Babu, M. Varma. Character Recognition in Natural Images. - In: Proceedings of the International Conference on Computer Vision Theory and Applications (VISAPP), Lisbon, Portugal, February 2009.]Search in Google Scholar