Skip to main content
Log in

A modified deep semantic binarization network for degradation removal in palm leaf manuscripts

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Palm leaves are the earliest forms of documentation for literature, showcasing rich traditions, philosophical insights, and scientific traditions in areas such as mathematics, medicine, agriculture, and martial arts, among others. This paper presents a deep semantic binarization network for enhancing 700-year-old Malayalam palm leaf manuscripts by addressing challenges such as uneven illumination, ink bleeds, stain marks, and brittleness. The learning model is trained with the ground truth data created using self-collected Malayalam palm leaf manuscripts, the Shiju Alex, and AMADI LONTAR degraded palm leaf manuscripts. The learning models are created by employing hyperparameter specifications of a fixed batch size of 32 with a learning rate of 0.00001, with epochs ranging from 100 to 500. Each learning model is analyzed by evaluating its performance using the proposed model, basic U-Net, and Sauvola Net on the datasets of AMADI LONTAR, Shiju Alex, and self-collected Malayalam manuscripts. The quantitative evaluation results show that the proposed model outperforms U-Net and the Sauvola Net models by achieving 90.55%, 0.205, and 90.44 of Accuracy, RMSE, and F-Measure towards validation set of self-collected datasets with batch sizes of 32 and 500 epochs. The data growth study conducted with varying training sample sizes shows a consistent increase in performance by the proposed model by achieving an accuracy of 90.55%, 88.26% precision, 70% recall, and 79% F-score towards validation of three datasets, demonstrating the effectiveness of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The datasets generated and analyzed during the current study are not publicly available as the work is related to a doctoral program study but are available from the corresponding author upon reasonable request.

References

  1. Li D, Wu Y, Zhou Y (2021) SauvolaNet: Learning adaptive Sauvola network for degraded document binarization. In: Document Analysis and Recognition – ICDAR 2021. Springer International Publishing, Cham, pp 538–553. https://doi.org/10.1007/978-3-030-86337-1_36

  2. Diringer D (2013) The book before printing: ancient, medieval and oriental. Courier Corporation

  3. Shi Z, Setlur S, Govindaraju V (2004) Digital enhancement of palm leaf manuscript images using normalization techniques. In: 5th international conference on knowledge based computer systems, pp 19–22

  4. Seebass T, Hinzler HIR (2015) Catalogue raisonné of the Balinese palm-leaf manuscripts with music notation. G. Henle Verlag, München

    Google Scholar 

  5. Bhatti UA, Zeeshan Z, Nizamani MM, Bazai S, Yu Z, Yuan L (2022) Assessing the change of ambient air quality patterns in Jiangsu Province of China pre-to post-COVID-19. Chemosphere 288:132569

    Article  Google Scholar 

  6. Aamir M, Li Z, Bazai S, Wagan RA, Bhatti UA, Nizamani MM, Akram S (2021) Spatiotemporal change of air-quality patterns in Hubei province—a pre-to post-COVID-19 analysis using path analysis and regression. Atmosphere 12(10):1338

    Article  Google Scholar 

  7. Subramani K, Subramaniam M (2021) Creation of original Tamil character dataset through segregation of ancient palm leaf manuscripts in medicine. Expert Systems, 38(1), n/a–n/a. https://doi.org/10.1111/exsy.12538.

  8. Kang S, Iwana BK, Uchida S (2021) Complex image processing with less data—document image binarization by integrating multiple pre-trained U-Net modules. Pattern Recogn 109:107577. https://doi.org/10.1016/j.patcog.2020.107577

    Article  Google Scholar 

  9. Calvo-Zaragoza J, Vigliensoni G, Fujinaga I (2017) Pixel-wise binarization of musical documents with convolutional neural networks. 2017 fifteenth IAPR international conference on machine vision applications (MVA). MVA Organization, pp 362–365. https://doi.org/10.23919/MVA.2017.7986876

  10. Arora S, Jahirabadkar S, Kulkarni A (2018) GPU Approach for handwritten Devanagari document binarization. Smart innovations in communication and computational sciences. Springer Singapore, Singapore, pp 299–308. https://doi.org/10.1007/978-981-10-8971-8_27

  11. Cherala S, Rege P (2008) Palm leaf manuscript/color document image enhancement by using improved adaptive binarization method. 2008 sixth Indian conference on computer vision, graphics & image processing. IEEE, pp 687–692. https://doi.org/10.1109/ICVGIP.2008.64

  12. Shi Z, Setlur S, Govindaraju V (2005) Digital image enhancement using normalization techniques and their application to palm leaf manuscripts. In: SPIE

  13. Kesiman MWA, Valy D, Burie JC, Paulus E, Suryani M, Hadi S … Ogier J-M (2018) ICFHR 2018 competition on document image analysis tasks for southeast asian palm leaf manuscripts. 2018 16th international conference on frontiers in handwriting recognition (ICFHR). IEEE, pp 483–488. https://doi.org/10.1109/ICFHR-2018.2018.00090

  14. Burie JC, Coustaty M, Hadi S, Kesiman MWA, Ogier J-M, Paulus E … Valy (2016) ICFHR2016 competition on the analysis of handwritten text in images of balinese palm leaf manuscripts. 2016 15th international conference on frontiers in handwriting recognition (ICFHR). IEEE, pp 596–601. https://doi.org/10.1109/ICFHR.2016.0114

  15. Chamchong R, Fung C (2011) Character segmentation from ancient palm leaf manuscripts in Thailand. Proceedings of the 2011 workshop on historical document imaging and processing. ACM, pp 140–145. https://doi.org/10.1145/2037342.2037366

  16. Sudarsan D, Vijayakumar P, Biju S, Sanu S, Shivadas SK (2018). Digitalization of Malayalam palmleaf manuscripts based on contrast-based adaptive binarization and convolutional neural networks. 2018 international conference on wireless communications, signal processing and networking (WiSPNET). IEEE, pp 1–4. https://doi.org/10.1109/WiSPNET.2018.8538588

  17. Calvo-Zaragoza J, Gallego A-J (2019) A selectional auto-encoder approach for document image binarization. Pattern Recogn 86:37–47. https://doi.org/10.1016/j.patcog.2018.08.011

    Article  Google Scholar 

  18. BN BJ, Nair AS (2021) Ancient horoscopic palm leaf binarization using a deep binarization model - RESNET. 2021 5th international conference on computing methodologies and communication (ICCMC). IEEE, pp 1524–1529. https://doi.org/10.1109/ICCMC51019.2021.9418461

  19. Yu P, Li H, Ge P, Zhou H (2016) A binarization method for palm leaf manuscripts. In: 2016 8th international conference on intelligent human-machine systems and cybernetics (IHMSC), vol 2. IEEE, pp 174–178. https://doi.org/10.1109/ihmsc.2016.198

  20. Singh M, Indu S (2023) Denoising of palm leaf manuscripts using Gaussian filter and conservative smoothing. In: AIP conference proceedings, vol 2521, no 1. AIP Publishing. https://doi.org/10.1063/5.0142237

  21. Sudarsan D, Sankar D (2022) A novel complete denoising solution for old malayalam palm leaf manuscripts. Pattern Recognit Image Anal 32(1):187–204. https://doi.org/10.1134/S1054661822010096

    Article  Google Scholar 

  22. Alexander TJ, Kumar SS (2020) A novel binarization technique based on whale optimization algorithm for better restoration of palm leaf manuscript. J Ambient Intell Humaniz Comput:1–8. https://doi.org/10.1007/s12652-020-02546-2

  23. Westphal F, Lavesson N, Grahn H (2018) Document image binarization using recurrent neural networks. 2018 13th IAPR international workshop on document analysis systems (DAS). IEEE, pp 263–268. https://doi.org/10.1109/DAS.2018.71

  24. Sulaiman A, Omar K, Nasrudin MF (2019) Degraded historical document binarization: a review on issues, challenges, techniques, and future directions. J of Imaging 5(4):48. https://doi.org/10.3390/jimaging5040048

    Article  Google Scholar 

  25. Dang QV, Lee GS (2021) Document image binarization with stroke boundary feature guided network. IEEE Access 9:36924–36936. https://doi.org/10.1109/ACCESS.2021.3062904

    Article  Google Scholar 

  26. De R, Chakraborty A, Sarkar R (2020) Document image binarization using dual discriminator generative adversarial networks. IEEE Signal Process Lett 27:1090–1094. https://doi.org/10.1109/LSP.2020.3003828

    Article  Google Scholar 

  27. Huang X, Li L, Liu R, Xu C, Ye M (2020) Binarization of degraded document images with global-local U-Nets. Optik (Stuttgart) 203:164025. https://doi.org/10.1016/j.ijleo.2019.164025

    Article  Google Scholar 

  28. He S, Schomaker L (2019) DeepOtsu: document enhancement and binarization using iterative deep learning. Pattern Recogn 91:379–390. https://doi.org/10.1016/j.patcog.2019.01.025

    Article  Google Scholar 

  29. Ayyalasomayajula KR, Malmberg F, Brun A (2019) PDNet: semantic segmentation integrated with a primal-dual network for document binarization. Pattern Recogn Lett 121:52–60. https://doi.org/10.1016/j.patrec.2018.05.011

    Article  Google Scholar 

  30. Elfattah MA, Hassanien AE, Abuelenin S, Bhattacharyya S (2019) Multi-verse optimization clustering algorithm for binarization of handwritten documents. In: Recent trends in signal and image processing: ISSIP 2017. Springer Singapore, pp 165–175. https://doi.org/10.1007/978-981-10-8863-6_17

  31. Bezmaternykh P, Ilin D, Nikolaev D (2019) U-Net-bin: hacking the document image binarization contest. Кoмпьютepнaя oптикa 43(5):825–832. https://doi.org/10.18287/2412-6179-2019-43-5-825-832

    Article  Google Scholar 

  32. Tensmeyer C, Martinez T (2017) Document image binarization with fully convolutional neural networks. 2017 14th IAPR international conference on document analysis and recognition (ICDAR), 1. IEEE, pp 99–104. https://doi.org/10.1109/ICDAR.2017.25

  33. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. medical image computing and computer-assisted intervention – MICCAI 2015. Springer International Publishing, Cham, pp 234–241. https://doi.org/10.1007/978-3-319-24574-4_28

  34. Bannigidad P, Gudada C (2017) Restoration of degraded Kannada handwritten paper inscriptions (Hastaprati) using image enhancement techniques. 2017 International conference on computer communication and informatics (ICCCI). IEEE, pp 1–6. https://doi.org/10.1109/ICCCI.2017.8117697

  35. Shobha Rani N, Sajan Jain A, Kiran HR (2019) A unified preprocessing technique for enhancement of degraded document images. In: Proceedings of the international conference on ISMAC in computational vision and bio-engineering 2018 (ISMAC-CVB). Springer International Publishing, Cham, pp 221–233. https://doi.org/10.1007/978-3-030-00665-5_23

  36. Jia F, Shi C, He K, Wang C, Xiao B (2018) Degraded document image binarization using structural symmetry of strokes. Pattern Recogn 74:225–240. https://doi.org/10.1016/j.patcog.2017.09.032

    Article  Google Scholar 

  37. Jino PJ, Balakrishnan K (2017) Combined approach for binarization of offline handwritten documents. 2017 4th international conference on electronics and communication systems (ICECS). IEEE, pp 23–27. https://doi.org/10.1109/ECS.2017.8067873

  38. Boudraa O, Hidouci WK, Michelucci D (2017) A robust multi stage technique for image binarization of degraded historical documents. 2017 5th international conference on electrical engineering - boumerdes (ICEE-B). IEEE, pp 1–6. https://doi.org/10.1109/ICEE-B.2017.8192044

  39. Roy S, Bhattacharyya D, Bandyopadhyay SK, Kim TH (2017) An improved brain MR image binarization method as a preprocessing for abnormality detection and features extraction. Front Comp Sci 11:717–727. https://doi.org/10.1007/s11704-016-5129-y

    Article  Google Scholar 

  40. Roy S, Saha S, Dey A, Shaikh SH, Chaki N (2014) Performance evaluation of multiple image binarization algorithms using multiple metrics on standard image databases. In: ICT and critical infrastructure: proceedings of the 48th annual convention of computer society of india-vol II: hosted by CSI Vishakapatnam Chapter. Springer International Publishing, pp 349–360. https://doi.org/10.1007/978-3-319-03095-1_38

  41. Gangopadhyay T, Halder S, Dasgupta P, Chatterjee K, Ganguly D, Sarkar S, Roy S (2022) MTSE U-Net: an architecture for segmentation, and prediction of fetal brain and gestational age from MRI of brain. Netw Model Anal Health Inform Bioinform 11(1):50. https://doi.org/10.1007/s13721-022-00394-y

    Article  Google Scholar 

  42. Halder S, Gangopadhyay T, Dasgupta P, Chatterjee K, Ganguly D, Sarkar S, Roy S (2023) Fetal brain component segmentation using 2-way ensemble U-Net. In: International conference on data management, analytics & innovation. Springer Nature Singapore, Singapore, pp 367–382. https://doi.org/10.3389/fnins.2022.887634

  43. Pal D, Reddy PB, Roy S (2022) Attention UW-Net: a fully connected model for automatic segmentation and annotation of chest X-ray. Comput Biol Med 150:106083. https://doi.org/10.1016/j.compbiomed.2022.106083

    Article  Google Scholar 

  44. Kabiraj A, Pal D, Ganguly D, Chatterjee K, Roy S (2023) Number plate recognition from enhanced super-resolution using generative adversarial network. Multimed Tools Appl 82(9):13837–13853. https://doi.org/10.1007/s11042-022-14018-0

    Article  Google Scholar 

  45. Zhang J, Li C, Kosov S, Grzegorzek M, Shirahama K, Jiang T … Li H (2021) LCU-Net: a novel low-cost U-Net for environmental microorganism image segmentation. Pattern Recognit 115:107885. https://doi.org/10.1016/j.patcog.2021.107885

  46. Suryani M, Paulus E, Hadi S, Darsa UA, Burie JC (2017) The handwritten sundanese palm leaf manuscript dataset from 15th century. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR), vol 1. IEEE, pp 796–800. https://doi.org/10.1109/ICDAR.2017.135

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. Shobha Rani.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

J, B.N.B., Rani, N.S. A modified deep semantic binarization network for degradation removal in palm leaf manuscripts. Multimed Tools Appl 83, 62937–62969 (2024). https://doi.org/10.1007/s11042-023-18020-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-18020-y

Keywords

Navigation