Mobile-based text recognition from water quality devices

Shanti Dhakal; Maryam Rahnemoonfar

doi:10.1117/12.2081234

11 March 2015 Mobile-based text recognition from water quality devices

Shanti Dhakal, Maryam Rahnemoonfar

Proceedings Volume 9411, Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 2015; 941105 (2015) https://doi.org/10.1117/12.2081234
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

Abstract

Measuring water quality of bays, estuaries, and gulfs is a complicated and time-consuming process. YSI Sonde is an instrument used to measure water quality parameters such as pH, temperature, salinity, and dissolved oxygen. This instrument is taken to water bodies in a boat trip and researchers note down different parameters displayed by the instrument’s display monitor. In this project, a mobile application is developed for Android platform that allows a user to take a picture of the YSI Sonde monitor, extract text from the image and store it in a file on the phone. The image captured by the application is first processed to remove perspective distortion. Probabilistic Hough line transform is used to identify lines in the image and the corner of the image is then obtained by determining the intersection of the detected horizontal and vertical lines. The image is warped using the perspective transformation matrix, obtained from the corner points of the source image and the destination image, hence, removing the perspective distortion. Mathematical morphology operation, black-hat is used to correct the shading of the image. The image is binarized using Otsu’s binarization technique and is then passed to the Optical Character Recognition (OCR) software for character recognition. The extracted information is stored in a file on the phone and can be retrieved later for analysis. The algorithm was tested on 60 different images of YSI Sonde with different perspective features and shading. Experimental results, in comparison to ground-truth results, demonstrate the effectiveness of the proposed method.

Citation Download Citation

Shanti Dhakal and Maryam Rahnemoonfar "Mobile-based text recognition from water quality devices", Proc. SPIE 9411, Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 2015, 941105 (11 March 2015); https://doi.org/10.1117/12.2081234

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KEYWORDS

Optical character recognition

Image segmentation

Image processing

Distortion

Detection and tracking algorithms

Image enhancement

Java

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