Skip to main content
SpringerLink
Log in

Background grid extraction from historical hand-drawn cadastral maps

  • Original Paper
  • Published:
International Journal on Document Analysis and Recognition (IJDAR) Aims and scope Submit manuscript

Abstract

We tackle a novel problem of detecting background grids in hand-drawn cadastral maps. Grid extraction is necessary for accessing and contextualizing the actual map content. The problem is challenging since the background grid is the bottommost map layer that is severely occluded by subsequent map layers. We present a novel automatic method for robust, bottom-up extraction of background grid structures in historical cadastral maps. The proposed algorithm extracts grid structures under significant occlusion, missing information, and noise by iteratively providing an increasingly refined estimate of the grid structure. The key idea is to exploit periodicity of background grid lines to corroborate the existence of each other. We also present an automatic scheme for determining the ‘gridness’ of any detected grid so that the proposed method self-evaluates its result as being good or poor without using ground truth. We present empirical evidence to show that the proposed gridness measure is a good indicator of quality. On a dataset of 268 historical cadastral maps with resolution \(1424\times 2136\) pixels, the proposed method detects grids in 247 images yielding an average root-mean-square error (RMSE) of 5.0 pixels and average intersection over union (IoU) of 0.990. On grids self-evaluated as being good, we report average RMSE of 4.39 pixels and average IoU of 0.991. To compare with the proposed bottom-up approach, we also develop three increasingly sophisticated top-down algorithms based on RANSAC-based model fitting. Experimental results show that our bottom-up algorithm yields better results than the top-down algorithms. We also demonstrate that using detected background grids for stitching different maps is visually better than both manual and SURF-based stitching.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Notes

  1. Drawn on top of the grid layer.

  2. We denote our complete grid extraction pipeline by the same name.

References

  1. Fan, K.-C., Liu, C.-H., Wang, Y.-K.: Segmentation and classification of mixed text/graphics/image documents. Pattern Recognit. Lett. 15(12), 1201–1209 (1994)

    Article  Google Scholar 

  2. Kim, N.W., Lee, J., Lee, H., Seo, J.: Accurate segmentation of land regions in historical cadastral maps. J. Vis. Commun. Image Rep. 25(5), 1262–1274 (2014)

    Article  Google Scholar 

  3. Heikkonen, J., Mantynen, M.: A computer vision approach to digit recognition on pulp bales. Pattern Recognit. Lett. 17(4), 413–419 (1996)

    Article  Google Scholar 

  4. Nishida, H.: An approach to integration of off-line and on-line recognition of handwriting. Pattern Recognit. Lett. 16(11), 1213–1219 (1995)

    Article  Google Scholar 

  5. Saabni, R., Asi, A., El-Sana, J.: Text line extraction for historical document images. Pattern Recognit. Lett. 35, 23–33 (2014)

    Article  Google Scholar 

  6. Nagy, G.: Disruptive developments in document recognition. Pattern Recognit. Lett. 79, 106–112 (2016)

    Article  Google Scholar 

  7. Group, W.B.: Pakistan–Punjab Land Records Management and Information Systems Project (English). Technical report, World Bank Group (2017). http://documents.worldbank.org/curated/en/488611499886532325/. Accessed 16 Dec 2022

  8. Leyk, S.: Segmentation of colour layers in historical maps based on hierarchical colour sampling. In: Ogier, J.-M., Liu, W., Lladós, J. (eds.) Graphics Recognition. Achievements, Challenges, and Evolution, pp. 231–241. Springer, Berlin (2010)

  9. Chen, H.-H., Chuang, W.-N., Wang, C.-C.: Vision-based line detection for underwater inspection of breakwater construction using an ROV. Ocean Eng. 109, 20–33 (2015)

    Article  Google Scholar 

  10. Garai, A., Biswas, S., Mandal, S., Chaudhuri, B.B.: Automatic rectification of warped bangla document images. IET Image Proc. 14(1), 74–83 (2020)

    Article  Google Scholar 

  11. Khade, R., Jariwala, K., Chattopadhyay, C., Pal, U.: A rotation and scale invariant approach for multi-oriented floor plan image retrieval. Pattern Recognit. Lett. 145, 1–7 (2021)

    Article  Google Scholar 

  12. Arias, J.F., Lai, C.P., Surya, S., Kasturi, R., Chhabra, A.: Interpretation of telephone system manhole drawings. Pattern Recognit. Lett. 16(4), 355–369 (1995)

    Article  Google Scholar 

  13. Samet, H., Soffer, A.: Marco: map retrieval by content. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 783–798 (1996)

    Article  Google Scholar 

  14. Samet, H., Soffer, A.: MAGELLAN: map acquisition of geographic labels by legend analysis. Int. J. Doc. Anal. Recognit. 1(2), 89–101 (1998)

    Article  Google Scholar 

  15. Santos, R., Ohashi, T., Yoshida, T., Ejima, T.: Filtering and segmentation of digitized land use map images. Int. J. Doc. Anal. Recognit. 1(3), 167–174 (1998)

    Article  Google Scholar 

  16. Li, L., Nagy, G., Samal, A., Seth, S., Xu, Y.: Integrated text and line-art extraction from a topographic map. Int. J. Doc. Anal. Recognit. 2(4), 177–185 (2000)

    Article  Google Scholar 

  17. Dhar, D.B., Chanda, B.: Extraction and recognition of geographical features from paper maps. Int. J. Doc. Anal. Recognit. 8(4), 232–245 (2006)

    Article  Google Scholar 

  18. Chiang, Y.-Y., Knoblock, C.A.: A general approach for extracting road vector data from raster maps. Int. J. Doc. Anal. Recognit. 16(1), 55–81 (2013)

  19. Tam, K.Y., Lay, J.A., Levy, D.: Automatic grid segmentation of populated chessboard taken at a lower angle view. In: 2008 Digital Image Computing: Techniques and Applications, pp. 294–299. IEEE (2008)

  20. Czyzewski, M.A., Laskowski, A., Wasik, S.: Chessboard and chess piece recognition with the support of neural networks (2017). arXiv preprint arXiv:1708.03898

  21. Duda, R.O., Hart, P.E.: Use of the hough transformation to detect lines and curves in pictures. Technical report, SRI International Menlo Park CA AI Center (1971)

  22. Zheng, Y., Li, H., Doermann, D.: A model-based line detection algorithm in documents. In: International Conference on Document Analysis and Recognition, pp. 44–48. IEEE (2003)

  23. Zheng, Y., Li, H., Doermann, D.: Background line detection with a stochastic model. In: Conference on Computer Vision and Pattern Recognition Workshop, vol. 3, pp. 23–23. IEEE (2003)

  24. Chen, J., Lopresti, D.: Model-based ruling line detection in noisy handwritten documents. Pattern Recognit. Lett. 35, 34–45 (2014)

    Article  Google Scholar 

  25. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  26. Khan, S., Aslam, A., Ahmad, S., Rehan, A., Gul, A., Alam, U., Naqvi, H., Bukhari, Z., Iqbal, I., Sherdil, K.: Mapping Rural Pakistan: Bottlenecks and Solutions. International Growth Centre, London (2011)

    Google Scholar 

  27. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall Inc, USA (2006)

    Google Scholar 

  28. Zuiderveld, K.: Contrast limited adaptive histogram equalization. Graphics gems 474–485 (1994)

  29. von Gioi, R.G., Jakubowicz, J., Morel, J.-M., Randall, G.: LSD: a fast line segment detector with a false detection control. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 722–732 (2010)

    Article  Google Scholar 

  30. Akinlar, C., Topal, C.: EDLines: a real-time line segment detector with a false detection control. Pattern Recognit. Lett. 32(13), 1633–1642 (2011)

    Article  Google Scholar 

  31. Hamid, N., Khan, N.: LSM: perceptually accurate line segment merging. J. Electr. Imaging 25(6), 061620 (2016)

    Article  Google Scholar 

  32. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: European Conference on Computer Vision, pp. 404–417. Springer (2006)

Download references

Acknowledgements

This research has been supported by HEC-NRPU Grant 8329 titled ‘DoCMap: Digitization of Cadastral Maps.’ The original formulation of RANSAC version R1 was developed by Hafee Atyub.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of the Punjab, Mall Road, Lahore, Punjab, 54200, Pakistan

    Tauseef Iftikhar & Nazar Khan

Authors
  1. Tauseef Iftikhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nazar Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nazar Khan.

Additional information

Publisher's Note

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

Appendix: Algorithms

Appendix: Algorithms

Algorithm 1
figure b

RVD: Refinement via diagonal lines.

Full size image
Algorithm 2
figure c

DCC: Distance Constrained Clustering

Full size image
Algorithm 3
figure d

ICE: Iterative Corroborative Evidence

Full size image
Algorithm 4
figure e

LVD: Grid lines via diagonal lines.

Full size image
Algorithm 5
figure f

DICE: Diagonal Iterative Corroborative Evidence

Full size image
Algorithm 6
figure g

RANSAC Version 1: Grid lines via segments in a fixed orientation.

Full size image
Algorithm 7
figure h

RANSAC Version 2: Grid lines via segments in all grid orientations.

Full size image
Algorithm 8
figure i

RANSAC Version 3: Grid lines via segments in all grid orientations and known grid width.

Full size image

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

Iftikhar, T., Khan, N. Background grid extraction from historical hand-drawn cadastral maps. IJDAR (2023). https://doi.org/10.1007/s10032-023-00457-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10032-023-00457-4

Keywords

Search

Navigation