A Low cost Data Acquisition System From Digital Display Instruments Employing Image Processing Technique

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— The use of digital instruments in industries and laboratories is rapidly increasing as they are simple to calibrate and have relatively high precision. In this paper, an automatic data acquisition system is proposed using OCR technique from digital multi-meter and other similar digital display devices. The input image is taken from a digital multi-meter having LCD seven segment display using a web cam. The image is then processed to extract numeric digits which are recognized using a feedforward neural network. The recognized values may be then exported to a spreadsheet for graph plotting and further analysis. A distinct advantage of this method is that it can automatically detect decimal point as well as negative sign. This setup can be used in real time systems employing a wide variety of digital display instruments, with high accuracy. Keywords — OCR, Adaptive Thresholding, Data acquisition I. INTRODUCTION Measuring devices in the laboratory as well as in some industries mostly have a display unit through which the output is taken. The process of output data collection from display is usually done manually or by data acquisition cards which may not be always available. Moreover, commonly available data acquisition cards are very costly. So a solution of this data acquisition problem is proposed using a webcam and processing unit which can be arranged for at a nominal cost. Further it can eliminate the error of human eye as well as adjust the interval of taking the reading. The main objective in OCR technique is to distinguish the object of interest from the original image. In order to achieve that, the noise within the image has to be removed up to a certain level without deforming the character to be r... ... middle of paper ... ... S. J. Perantonis, B. Gatos and N. Papamarkos “Image Segmentation and linear feature identification using rectangular block decomposition” ICECS ’96 Proceedings of the Third International Conference on Electronics, Circuits and Systems, pp-183-186. [13] Shaoyuan Sun, Haitao Zhao “Kernel Averaging Filter” 2008. CISP ‘08 Congress on Image and Signal Processing, pp-681-685. [14] Derong Liu, Tsu-Shuan Chang, and Yi Zhang, “A constructive algorithm for feedforward neural networks with incremental training”, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, vol - 49 no - 12, pp - 1876-1879. [15] Nallasamy Mani and Bala Srinivasan. “Application of Artificial Neural Network Model for Optical Character Recognition” 1997 IEEE International Conference on Systems, Man and Cybernetics and Simulation, vol - 3, pp-2517-2520.

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