Document Image Binarization and Segmentation

Shashidhar Bhat, Prof. Vinod H C, Shaili Srivastava, Shashikumar N, Ms Tulika

Abstract


Conceptually the Binarization of the chronicled archives is NP-difficult issue since the picture contains commotion, source debasements, and enlightenment. The point of binarization is to locate the best possible picture pixels' limit to enhance the general execution of the framework. This paper presents another half and half meta-heuristic calculation to decide the best edge an incentive for picture archives binarization. The point of Binarization is to locate the correct picture pixels' limit to enhance the general execution of the framework. Record division is a strategy for ripping the archive into unmistakable areas. In this proposed framework at first we displaying Wavelet deterioration and to binarize the record picture, and furthermore utilizes the projection profile to section lines and associated part investigation to fragment the characters. The normal result will be the binarized and fragmented characters, these character can be bolster to OCR for acknowledgement.

Full Text:

PDF

References


Mohammed Mudhsh, Shengwu Xiong “Hybrid swarm Optimisation for Document Image Binarisation based on Otsu Function “, CASA 2017.

Jino PJ, ”Combined Approach for Binarisation of offline Handwritten Documents”, IEEE 2017.

Zhongi Wang, Jin Zhang, Jing Huang,”Multi-granularity Hierarchial topic based Segmentation of structured, digital library resources”, The electronic library, 2017.

M W Lin, J R Tapamo, B Ndovie,”a Texture based method for Document Segmentation and Classification”, University Kwa-Zulu-Natal, 2017

Jino P J, Kannan Balakrishnan, “Combined Approach for Binarization of Offline Handwritten Documents” 4th International Conference on Electronics and Communication System (ICECS), 2017.

Michele Alberti, Manuel Bouillon, Rolf Ingold, Marcus Liwicki, “Open Evaluation Tool for Layout Analysis of Document Images”, arXiv:1712.01656v1 [cs.CV] 23 Nov 2017.

Christoph Wick and Frank Puppe, “Fully Convolutional Networks for Page Segmentation of Historical Document Images”, arXiv:1711.07695v1 [cs.CV] 21 Nov 2017.

Priyadharshini N, Vijaya MS, “Genetic Programming for Document Segmentation and Region Classification Using Discipulus”, (IJARAI) International Journal of Advanced Research in Artificial Intelligence, Vol. 2, No. 2, 2013.

J. Sauvola and M. Pietika inen, “Adaptive document image binarization,” Pattern recognition, vol. 33, no. 2, pp. 225–236, 2000.

N. Otsu, “A threshold selection method from gray-level histograms,”

Automat- ica, vol. 11, no. 285-296, pp. 23–27, 1975.

Most of our works have been referred from google.co.in


Refbacks

  • There are currently no refbacks.