Land Map Image Dataset: Ground-Truth And Classification Using Visual And Textural Features

Open access

Abstract

Research on document image analysis is actively pursued in the last few decades and services like OCR, vectorization of drawings/graphics and various types of form processing are very common. Handwritten documents, old historical documents and documents captured through camera are now being the subjects of active research. However, another very important type of paper document, namely the map document image processing research suffers due to the inherent complexities of the map document and also for nonavailability of benchmark public data-sets. This paper presents a new data-set, namely, the Land Map Image Database (LMIDb) that consists of a variety of land maps images (446 images at present and growing; scanned at 200/300 dpi in TIF format) and the corresponding ground-truth. Using semiautomatic tools non-text part of the images are deleted and the text-only ground-truth is also kept in the database. This paper also presents a classification strategy for map images using which the maps in the database are automatically classified into Political (Po), Physical (Ph), Resource (R) and Topographic (T) maps. The automatic classification of maps help indexing of the images in LMIDb for archival and easy retrieval of the right maps to get the appropriate geographical information. Classification accuracy is also tested on the proposed data-set and the result is encouraging.

[1] Agam, G., Argamon, S., Frieder, O., Grossman, D., Lewis, D. (2006). The Complex Document Image Processing (CDIP) test collection. Illinois Institute of Technology

[2] Arai, H., Abe, S., Nagura, M. (1993, October). Intelligent interactive map recognition using neural networks. In Document Analysis and Recognition, 1993., Proceedings of the Second International Conference on IEEE, 922–925

[3] Chaudhuri, B. B., Pal, U. (1997). An OCR system to read two Indian language scripts: Bangla and Devnagari (Hindi). In Document Analysis and Recognition, 1997., Proceedings of the Fourth International Conference on IEEE, 2, 1011–1015

[4] Epshtein, B., Ofek, E., Wexler, Y. (2010). Detecting text in natural scenes with stroke width transform. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on IEEE, 2963–2970

[5] Fletcher, L. A., Kasturi, R. (1988). A robust algorithm for text string separation from mixed text/-graphics images. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 10(6), 910-918

[6] Gatos, B., Pratikakis, I., Perantonis, S. J. (2006). Adaptive degraded document image binarization. Pattern recognition, 39(3), 317-327

[7] Gatos, B., Ntirogiannis, K., Pratikakis, I. (2009). ICDAR 2009 Document Image Binarization Contest (DIBCO 2009). In ICDAR, 9, 1375–1382

[8] Lazzara, G., Géraud, T. (2014). Efficient multiscale Sauvola’s binarization. International Journal on Document Analysis and Recognition (IJDAR), 17(2), 105–123

[9] Lee, S., Cho, M. S., Jung, K., Kim, J. H. (2010). Scene Text Extraction with Edge Constraint and Text Collinearity. In ICPR, 3983–3986

[10] Lewis, D., Agam, G., Argamon, S., Frieder, O., Grossman, D., Heard, J. (2006). Building a test collection for complex document information processing. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval ACM, 665–666

[11] Li, L., Nagy, G., Samal, A., Seth, S., Xu, Y. (1999, September). Cooperative text and line-art extraction from a topographic map. In Document Analysis and Recognition, 1999. ICDAR’99. Proceedings of the Fifth International Conference on IEEE, 467–470

[12] Lu, S., Su, B., Tan, C. L. (2010). Document image binarization using background estimation and stroke edges. International Journal on Document Analysis and Recognition (IJDAR), 13(4), 303-314

[13] Lucas, S. M., Panaretos, A., Sosa, L., Tang, A., Wong, S., Young, R., ... Lin, X. (2005). ICDAR 2003 robust reading competitions: entries, results, and future directions. International Journal of Document Analysis and Recognition (IJDAR), 7(2-3), 105–122

[14] De Coene, K., Ongena, T., Stragier, F., Vervust, S., Bracke, W., De Maeyer, P. (2012). Ferraris, the legend. The Cartographic Journal, 49(1), 30–42

[15] Nagy, R., Dicker, A., Meyer-Wegener, K. (2012). NEOCR: A configurable dataset for natural image text recognition. In Camera-Based Document Analysis and Recognition. Springer Berlin Heidelberg, 150–163

[16] Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A. Y. (2011). Reading digits in natural images with unsupervised feature learning. In NIPS workshop on deep learning and unsupervised feature learning, 2011(2), 5

[17] Niblack, W. (1985). An introduction to digital image processing. Strandberg Publishing Company

[18] Otsu, N. (1975). A threshold selection method from gray-level histograms. Automatica, 11(285–296), 23–27

[19] Ramírez-Ortegón, M. A., Tapia, E., Ramírez-Ramírez, L. L., Rojas, R., Cuevas, E. (2010). Transition pixel: A concept for binarization based on edge detection and gray-intensity histograms. Pattern Recognition, 43(4), 1233–1243

[20] Sauvola, J., Pietikȧinen, M. (2000). Adaptive document image binarization. Pattern recognition, 33(2), 225-236

[21] University of California, San Francisco. (2007). The Legacy Tobacco Document Library (LTDL)

[22] Yao, C., Bai, X., Liu, W., Ma, Y., Tu, Z. (2012, June). Detecting texts of arbitrary orientations in natural images. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on IEEE, 1083–1090

[23] Zhou, L., Lu, Y., Tan, C. L. (2006). Bangla/English script identification based on analysis of connected component profiles. In Document Analysis Systems VII. Springer Berlin Heidelberg. 243–254

Image Processing & Communications

The Journal of University of Technology and Life Sciences in Bydgoszcz

Journal Information

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 323 254 23
PDF Downloads 180 158 14