Enhancement and Retrieval of Historic Inscription Images

In this paper we have presented a technique for enhancement and retrieval of historic inscription images. Inscription images in general have no distinction between the text layer and background layer due to absence of color difference and possess highly correlated signals and noise; pertaining to which retrieval of such images using search based on feature matching returns inaccurate results. Hence, there is a need to first enhance the readability and then binarize the images to create a digital database for retrieval. Our technique provides a suitable method for the same, by separating the text layer from the non-text layer using the proposed cumulants based Blind Source Extraction(BSE) method, and store them in a digital library with their corresponding historic information. These images are retrieved from database using image search based on Bag-of-Words(BoW) method.

[1]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[2]  Santanu Chaudhury,et al.  NGFICA Based Digitization of Historic Inscription Images , 2013 .

[3]  Yonatan Wexler,et al.  Detecting text in natural scenes with stroke width transform , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Thomas S. Huang,et al.  Modified Fourier Descriptors for Shape Representation - A Practical Approach , 1996 .

[5]  Jean-Michel Jolion,et al.  Text localization, enhancement and binarization in multimedia documents , 2002, Object recognition supported by user interaction for service robots.

[6]  Yasuo Matsuyama,et al.  Database retrieval for similar images using ICA and PCA bases , 2005, Eng. Appl. Artif. Intell..

[7]  Shih-Fu Chang,et al.  Image Retrieval: Current Techniques, Promising Directions, and Open Issues , 1999, J. Vis. Commun. Image Represent..

[8]  Sergio Cruces,et al.  From blind signal extraction to blind instantaneous signal separation: criteria, algorithms, and stability , 2004, IEEE Transactions on Neural Networks.

[9]  David S. Doermann,et al.  Progress in camera-based document image analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[10]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[11]  Bhabatosh Chanda,et al.  Machine reading of camera-held low quality text images: An ICA-based image enhancement approach for improving OCR accuracy , 2008, 2008 19th International Conference on Pattern Recognition.

[12]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[13]  C. V. Jawahar,et al.  Word Image Retrieval Using Bag of Visual Words , 2012, 2012 10th IAPR International Workshop on Document Analysis Systems.

[14]  Andrzej Cichocki,et al.  Adaptive Blind Signal and Image Processing - Learning Algorithms and Applications , 2002 .

[15]  Laurenz Wiskott,et al.  CuBICA: independent component analysis by simultaneous third- and fourth-order cumulant diagonalization , 2004, IEEE Transactions on Signal Processing.

[16]  Matthijs C. Dorst Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[17]  Anna Tonazzini,et al.  Independent component analysis for document restoration , 2004, Document Analysis and Recognition.

[18]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[19]  Robin Sibson,et al.  What is projection pursuit , 1987 .

[20]  Sergio Cruces,et al.  On a new blind signal extraction algorithm: different criteria and stability analysis , 2002, IEEE Signal Processing Letters.

[21]  Venu Govindaraju,et al.  Historical document image enhancement using background light intensity normalization , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..