Character Recognition Method for Low-Contrast Images of Numerical Instruments

In this paper, a method of preprocessing to extract numbers from a low-contrast image of numerical instruments and recognition based on skeleton structural features is proposed. The approach is based on the grayscale dilation operation which can enhance the difference of targets with the background of the image and eliminate the adhesion between the target and the environment. After that, a skeleton algorithm is used to highlight the shape and topology of the target which makes the target number easier to be identified. Using HALCON to test, the result shows that the identification has a high accuracy rate.

[1]  Meng Shi,et al.  Handwritten numeral recognition using gradient and curvature of gray scale image , 2002, Pattern Recognit..

[2]  T. G. Sitharam,et al.  OCR Prediction Using Support Vector Machine Based on Piezocone Data , 2008 .

[3]  Jiuqiang Han,et al.  HALCON application for shape-based matching , 2008, 2008 3rd IEEE Conference on Industrial Electronics and Applications.

[4]  Chuan Li Xi,et al.  Halcon-Based Optical Fiber Geometry Parameters Measurement Algorithm , 2013 .

[5]  Toby P. Breckon,et al.  Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab , 2011 .

[6]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Lili Rong,et al.  Building a three-layer BP neural network by fuzzy rules , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[8]  Brij Mohan Singh,et al.  Efficient binarization technique for severely degraded document images , 2014, CSI Transactions on ICT.

[9]  Shahrel Azmin Suandi,et al.  Malaysian Car Number Plate Detection System Based on Template Matching and Colour Information , 2010 .

[10]  Ping Zhang,et al.  A floating feature detector for handwritten numeral recognition , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[11]  Wenyu Liu,et al.  A Unified Framework for Multioriented Text Detection and Recognition , 2014, IEEE Transactions on Image Processing.

[12]  Wang Yu-hai,et al.  The Pre-processing of High-resolution Remote Sensing Image on Grey Morphology , 2004 .

[13]  Yu Long,et al.  Realization of vehicle License Plate character Recognition based On HALCON , 2011, 2011 4th International Congress on Image and Signal Processing.

[14]  Louis A. Tamburino,et al.  Efficient Algorithms for the Soft Morphological Operators , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  R. Sabourin,et al.  Handwritten digit segmentation: a comparative study , 2012, International Journal on Document Analysis and Recognition (IJDAR).

[16]  Zhang Nin.  Digital Recognition System of Numerical Instruments Based on Matlab and C , 2013 .