Abstract
For many years, the field of image processing and pattern approval has included handwriting approval among its most intriguing and rigid analytical fields. In this article, the steps necessary to convert text from a paper document to a computer-readable format has been discussed. This is the most tedious and labor-intensive task. For nearly three decades, scientists have been trying to figure out how to make a computer read like a human. In Optical Character Recognition (OCR), a scanned picture is converted mechanically or electronically into an image that may be read as handwritten, typed, or printed text. It's a way to turn paper documents into digital files that can be searched and utilised in automated procedures. To facilitate applications like machine translation, text-to-speech, and text mining, OCR encodes the pictures as machine-readable text. It's an easy and inexpensive approach to make OCR that can read any document in a standard font size and with standard handwriting.
References
O. Joshua, T. Ibiyemi, and B. Adu, “A Comprehensive Review On Various Types of Noise in Image Processing,” Int. J. Sci. Eng. Res., vol. 10, no. November, pp. 388–393, 2019.
U. Garain, A. Jain, A. Maity, and B. Chanda, “Machine reading of camera-held low quality text images: An ICA-based image enhancement approach for improving OCR accuracy,” Proc. - Int. Conf. Pattern Recognit., pp. 1–4, 2008, doi: 10.1109/icpr.2008.4761840.
C. H. Keerthana, P. S. S, S. S. Pai, V. A. Meda, and M. D. T H, “Character Recognition of Handwritten Text Using Machine Learning and Image Processing,” J. Opt. Commun. Electron., vol. 5, no. 2, pp. 11–16, 2019, doi: 10.5281/zenodo.2705098.
B. Altinoklu, I. Ulusoy, and S. Tari, “A probabilistic sparse skeleton based object detection,” Pattern Recognit. Lett., vol. 83, pp. 243–250, Nov. 2016, doi: 10.1016/j.patrec.2016.07.009.
M. Koistinen, K. Kettunen, and T. Pääkkönen, “Improving Optical Character Recognition of Finnish Historical Newspapers with a Combination of Fraktur & Antiqua Models and Image Preprocessing,” Proc. 21st Nord. Conf. Comput. Linguist., no. May, pp. 23–24, 2017.
S. Mandal, S. Chowdhury, A. Das and B. Chanda, “A Simple and Effective Table Detection system from Document Images,” Int’l J. Document Analysis and Recognition, vol. 8, nos. 2-3, pp. 172-182, June 2006.
F. Shafait, D. Keyser and T.M. Breuel, “Pixel-Accurate Representation and Evaluation of Page Segmentation in Document Images,” Proc. 18th Int’lConf. Pattern Recognition, pp. 872-875, Aug. 2006.
F. Shafait, J. van Beusekom , D. Keysers, and T.M. Breuel, “Page Frame Detection for Marginal Noise Removal from Scanned Documents,” Proc. Scandinavian Conf. Image Analysis, pp. 651-660, June 2007
N. Stamatopoulos, B. Gatos and A. Kesidis, “Automatic Borders Detection of Camera Document Images,” Proc. Second Int’l Workshop Camera-Based Document Analysis and Recognition, pp. 71-78, Sept. 2007.
D. Keysers, F. Shafait and T.M. Breuel, “Document Image Zone Classification—a Simple High-Performance Approach,” Proc. Second Int’l Conf. Computer Vision Theory and Applications, pp. 44-51, Mar. 2007.
G. Peng, P. Yu, H. Li and L. He, "Text line segmentation using Viterbi algorithm for the palm leaf manuscripts of Dai," 2016 International Conference on Audio, Language and Image Processing (ICALIP), Shanghai, 2016, pp. 336-340.
A. Sanjrani, J. Baber, M. Bakhtyar, W. Noor and M. Khalid, "Handwritten Optical Character Recognition system for Sindhi numerals," 2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube), Quetta, 2016, pp. 262-267.
S. F. Rashid, F. Shafait and T. M. Breuel, "Scanning Neural Network for Text Line Recognition," 2012 10th IAPR International Workshop on Document Analysis Systems, Gold Cost,QLD,2012,pp.105-109. doi: 10.1109/DAS.2012.77.
G. Siebra Lopes, D. Clifte da Silva, A. W. Oliveira Rodrigues and P. P. Reboucas Filho, "Recognition of handwritten digits using the signature features and Optimum-Path Forest Classifier," IN IEEE Latin America Transactions, vol. 14, no. 5, pp. 2455-2460, May 2016.
E. Hassan, S. Chaudhury and M. Gopal, "Multi-modal Information Integration for Document Retrieval," 2013 12th International Conference on Document Analysis and Recognition, Washington, DC, 2013, pp. 1200-1204.
T. Mantoro, A. M. Sobri and W. Usino, "Optical Character Recognition (OCR) Performance in Server-Based Mobile Environment," 2013 International Conference on Advanced Computer Science Applications and Technologies, Kuching, 2013, pp. 423-428.
Samantaray, R. K., Panda, S., & Pradhan, D. (2011). Application of Digital Image Processing and Analysis in Healthcare Based on Medical Palmistry. IJCA Special Issue on Intelligent Systems and Data Processing, 56-59.
Sharma, D. V., Saini, G., & Joshi, M. (2012). Statistical Feature Extraction Methods for Isolated Handwritten Gurumukhi Script. International Journal of Engineering Research and Application, 2(4), 380-384.
M. Zhang, F. Xie, J. Zhao, R. Sun, L. Zhang, and Y. Zhang, “Chinese license plates recognition method based on a robust and efcient feature extraction and bpnn algorithm,” Journal of Physics: Conference Series, vol. 1004, p. 012022, 2018.
Y. Yuan, W. Zou, Y. Zhao, X. Wang, X. Hu, and N. Komodakis, “A robust and efcient approach to license plate detection,” IEEE Transactions on Image Processing, vol. 26, no. 3, pp. 1102–1114, 2017.
Y. Wiseman, “Vehicle identifcation by OCR, RFID and Bluetooth for toll roads,” International Journal of Control and Automation, vol. 11, no. 9, pp. 1–12, 2018.
A. Saini, S. Chandok, and P. Deshwal, “Advancement of trafc management system using RFID,” in Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1254–1260, Madurai, June 2017
Q. Wang, “License plate recognition via convolutional neural networks,” in Proceedings of the 2017 8th IEEE International Conference on Sofware Engineering and Service Science (ICSESS), pp. 926–929, Beijing, China, November 2017.
R. Fu, “Te research and design of vehicle license plate recognition system in trafc management system,” International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 9, no. 3, pp. 445–456, 2016.