Image Compression of Handwritten Devanagari Text Documents Using a Convolutional Autoencoder
Keywords:
Autoencoder, Compression, CNN, Convolutional Autoencoder, Document Compression, Image CompressionAbstract
A large number of books/documents written in so many languages are available in digital libraries. These scanned copies of books/documents consume more space on hard disk. If the existing image compression technique like JPEG is used for compressing the textual images then significant amount of information could be lost. Hence we proposed and implemented the Convolutional Autoencoder (CAE) for compression and decompression of handwritten Devanagari document images. As long as compression of document images is concerned, relatively large work was reported for documents having printed text. Comparatively less significant work was done on handwritten Devanagari document images. Convolutional autoencoders have been shown to outperform traditional compression methods like JPEG in terms of compression efficiency and image quality, but requires more computational resources. Convolutional autoencoders have the potential to become a widely adopted approach for image compression in the future, as computing power increases and the demand for higher quality compressed images grows. A lot of experimentation was done by the changing the compression ratios. The PSNR and MSSIM parameters are considered to estimate the performance of compression and decompression results. The compression and decompression results achieved with the help of CAE are much encouraging as compared to the conventional image compression.
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S. V. Khangar, D. P. Bharne, and L. G. Malik, “Compression Strategy for Handwritten Gray Level Document Images,” Proc. - 2012 Int. Conf. Commun. Inf. Comput. Technol. ICCICT 2012, pp. 1–5, 2012.
P. S. Metkar and S. S. Thakare, “A Review of Scanned Handwritten Document Compression in Devnagari script,” pp. 457–459, 2018.
K. Satone, A. Deshmukh, and P. Ulhe, “A Review of Image Compression Techniques,” Proc. Int. Conf. Electron. Commun. Aerosp. Technol. ICECA 2017, vol. 2017-Janua, no. 1, pp. 97–101, 2017.
W. Xiao, N. Wan, A. Hong, and X. Chen, “A Fast JPEG Image Compression Algorithm Based on DCT,” Proc. - 2020 IEEE Int. Conf. Smart Cloud, SmartCloud 2020, pp. 106–110, 2020.
H. T. Sadeeq, T. H. Hameed, A. S. Abdi, and A. N. Abdulfatah, “Image Compression Using Neural Networks: A Review,” Int. J. online Biomed. Eng., vol. 17, no. 14, pp. 135–153, 2021.
G. K. Wallace, “The JPEG Still Picture Compression Standard” pp. 1–17, 1991.
M. Rabbani and R. Joshi, An Overview of the JPEG 2000 Still Image Compression Standard, vol. 17. 2002.
D. Xu and X. Bao, “High Efficient Compression Strategy For Scanned Receipts And Handwritten Documents,” 2009 1st Int. Conf. Inf. Sci. Eng. ICISE 2009, pp. 1270–1273, 2009.
U. Garain, A. Mandal, S. Debnath, and B. B. Chaudhuri, “Compression of Scan-Digitized Indian Language Printed Text: A Soft Pattern Matching Technique,” Proc. 2003 ACM Symp. Doc. Eng., pp. 185–192, 2003.
I. H. Witten, T. C. Bell, H. Emberson, S. Inglis, and A. Moffat, “Textual Image Compression: Two-Stage Lossy/Lossless Encoding of Textual Images,” Proc. IEEE, vol. 82, no. 6, pp. 878–888, 1994.
E. Agustsson, M. Tschannen, F. Mentzer, R. Timofte, and L. Van Gool, “Generative Adversarial Networks for Extreme Learned Image Compression,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2019-Octob, pp. 221–231, 2019.
Y. Sun, J. Chen, Q. Liu, and G. Liu, “Learning Image Compressed Sensing with Sub-Pixel Convolutional Generative Adversarial Network,” Pattern Recognit., vol. 98, p. 107051, 2020.
V. A. de Oliveira et al., “Reduced-Complexity End-to-End Variational Autoencoder for on Board Satellite Image Compression,” Remote Sens., vol. 13, no. 3, pp. 1–27, 2021.
S. Wen, J. Zhou, A. Nakagawa, K. Kazui, and Z. Tan, “Variational Autoencoder Based Image Compression with Pyramidal Features and Context Entropy Model,” IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., vol. 2019-June, pp. 4321–4324, 2019.
Y. Zhang, “A Better Autoencoder for Image : Convolutional Autoencoder,” pp. 1–7.
P. Vincent and H. Larochelle, “Extracting and Composing Robust Features with Denoising.pdf,” pp. 1096–1103, 2008.
Z. Cheng, H. Sun, M. Takeuchi, and J. Katto, “Deep Convolutional AutoEncoder-based Lossy Image Compression,” 2018 Pict. Coding Symp., pp. 253–257, 2018.
T. Dumas, C. Guillemot, and I. R. Bretagne-atlantique, “Autoencoder Based Image Compression : Can the Learning be Quantization Independent,” 2018 IEEE Int. Conf. Acoust. Speech Signal Process., no. 1, pp. 1188–1192, 2018.
N. Johnston, A. Gordon, and J. Ballé, “Computationally Efficient Neural Image Compression.”
Y. Raut, T. Tiwari, P. Pande, and P. Thakar, “Image Compression Using Convolutional Autoencoder,” Lect. Notes Electr. Eng., vol. 601, pp. 221–230, 2020.
P. Akyazi and T. Ebrahimi, “Learning-Based Image Compression using Convolutional Autoencoder and Wavelet Decomposition,” 2000.
Mondal, D., & Patil, S. S. (2022). EEG Signal Classification with Machine Learning model using PCA feature selection with Modified Hilbert transformation for Brain-Computer Interface Application. Machine Learning Applications in Engineering Education and Management, 2(1), 11–19. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/20
Anand, R., Ahamad, S., Veeraiah, V., Janardan, S. K., Dhabliya, D., Sindhwani, N., & Gupta, A. (2023). Optimizing 6G Wireless Network Security for Effective Communication. In Innovative Smart Materials Used in Wireless Communication Technology (pp. 1–20). IGI Global.
Mohan, D. ., & Nair, L. R. . (2023). A Robust Deep Model for Improved Categorization of Legal Documents for Predictive Analytics . International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 175–183. https://doi.org/10.17762/ijritcc.v11i3s.6179
Pande, S. D., Kanna, R. K., & Qureshi, I. (2022). Natural Language Processing Based on Name Entity With N-Gram Classifier Machine Learning Process Through GE-Based Hidden Markov Model. Machine Learning Applications in Engineering Education and Management, 2(1), 30–39. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/22
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