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Abstract

Python has developed into one of the most popular and powerful languages in coding and can be used for various purposes such as language detection. Language detection's idea is based on the identification of the characters used in words and expressions in the text. The main aim is to identify words commonly used in a particular language such as of or to in English. Python has several modules to enhance language identification. Language detection is a common feature in any Web application or Social Network, which is usually combined with Machine Translation to enhance content accessibility and user experience. This develops a platform for other features including document analysis, articles/tweets/posts, and Machine Translation. When the document is fed, the language is not known. As a result, conducting segmentation is not possible since the language in the document is unknown, which makes using word base models impossible. Consequently, the best option is the use of "Observa." One research area that aims to establish a computer system that can identify texts from images is text recognition in images. In today's world, there is a growing demand to transfer information from paper documents to the storage disks in computers for easier reuse and access of the information through the process of searching. A simple method of achieving this is through scanning the paper documents and then storing them in image form. However, it is extremely difficult to reuse the information by reading the contents and manually searching for what is needed from the images. Various challenges associated are poor image quality and the font of the document's characteristics. As a result of the challenges, the computer cannot identify the characters and read the document. The paper discusses various ways to recognize texts from images. The aim of this work is to identify texts from images to ensure that the reader can apply a certain structure of distinct processing modules for easier comprehension.

Keywords

detection processing comprehension characters

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Iman Kadhim Ajlan, Ahmed fadhil Bin yusof, Herlina Binti Abdul Rahim, & Ahmed Jameel Ismael. (2022). Text Recognition from Images. Texas Journal of Engineering and Technology, 10, 10–18. Retrieved from https://zienjournals.com/index.php/tjet/article/view/2098

References

  1. L. Weidinger, J. Mellor, M. Rauh, C. Griffin, J. Uesato, P.-S. Huang, M. Cheng, M. Glaese, B. Balle, and A. Kasirzadeh, "Ethical and social risks of harm from language models," arXiv preprint arXiv:.04359, 2021.
  2. J. Yang, K. Wang, J. Li, J. Jiao, and J. Xu, "A fast adaptive binarization method for complex scene images," in 2012 19th IEEE International Conference on Image Processing, 2012, pp. 1889-1892: IEEE.
  3. H. Salim, J. S. Qateef, A. M. Alaidi,and R. M. Al_airaji, "Face Patterns Analysis and recognition System based on Quantum Neural Network QNN," International Journal of Interactive Mobile Technologies (iJIM), vol. 16, no. 9, 2022.
  4. H. TH., and A. M. Alaidi, "Automated Cheating Detection based on Video Surveillance in the Examination Classes," International Journal of Interactive Mobile Technologies (iJIM), vol. 16, no. 10, 2022.
  5. S. Dutta, N. Sankaran, K. P. Sankar, and C. Jawahar, "Robust recognition of degraded documents using character n-grams," in 2012 10th IAPR International Workshop on Document Analysis Systems, 2012, pp. 130-134: IEEE.
  6. B. Majeed, Haider TH. Salim "Computational Thinking (CT) Among University Students," International Journal of Interactive Mobile Technologies (iJIM), vol. 16, no. 10, 2022.
  7. M. Rhead, R. Gurney, S. Ramalingam, and N. Cohen, "Accuracy of automatic number plate recognition (ANPR) and real world UK number plate problems," in 2012 IEEE international carnahan conference on security technology (ICCST), 2012, pp. 286-291: IEEE.
  8. S. H. Abbood, H. N. Abdull Hamed, M. S. Mohd Rahim, and A. H. M. Alaidi,, "DR-LL Gan: Diabetic Retinopathy Lesions Synthesis using Generative Adversarial Network," International Journal of Online Biomedical Engineering, vol. 18, no. 3, 2022.
  9. K.-S. Son, J.-W. Kim, and J.-H. Lim, "Convergence CCTV camera embedded with Deep Learning SW technology," Journal of the Korea Convergence Society, vol. 10, no. 1, pp. 103-113, 2019.
  10. B. Shi, M. Yang, X. Wang, P. Lyu, C. Yao, and X. Bai, "Aster: An attentional scene text recognizer with flexible rectification," IEEE transactions on pattern analysis machine intelligence, vol. 41, no. 9, pp. 2035-2048, 2018.
  11. S. Malakar, S. Halder, R. Sarkar, N. Das, S. Basu, and M. Nasipuri, "Text line extraction from handwritten document pages using spiral run length smearing algorithm," in 2012 international conference on communications, devices and intelligent systems (CODIS), 2012, pp. 616-619: IEEE.
  12. S. Stoliński and W. Bieniecki, "Application of OCR systems to processing and digitization of paper documents," Information Systems in Management VIII, vol. 102, 2011.
  13. K. Ntirogiannis, B. Gatos, and I. J. I. T. o. I. P. Pratikakis, "Performance evaluation methodology for historical document image binarization," vol. 22, no. 2, pp. 595-609, 2012.
  14. F. Albardi, H. D. Kabir, M. M. I. Bhuiyan, P. M. Kebria, A. Khosravi, and S. Nahavandi, "A comprehensive study on torchvision pre-trained models for fine-grained inter-species classification," in 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2021, pp. 2767-2774: IEEE.
  15. S. J. A. P. Gholizadeh, "Top Popular Python Libraries in Research," 2022.
  16. A. J. Clark, "Pillow (pil fork) documentation," 2015.
  17. S. Marcel and Y. Rodriguez, "Torchvision the machine-vision package of torch," in Proceedings of the 18th ACM international conference on Multimedia, 2010, pp. 1485-1488.
  18. J. Salvatier, T. V. Wiecki, and C. Fonnesbeck, "Probabilistic programming in Python using PyMC3," PeerJ Computer Science, vol. 2, p. e55, 2016.
  19. H. Singh, "Basics of Python and Scikit image," in Practical Machine Learning and Image Processing: Springer, 2019, pp. 29-61.