Amharic is an indigenous Ethiopic script which follows a unique syllabic writing system adopted from an ancient Geez script. In this paper, we introduce an end-to-end Amharic text-line image recognition approach based on recurrent neural networks. We propose an automated in-plane optical scaling calibration system for non-contact measurement. We developed a heuristic analytical method for calibrating an image by applying several filters to extract the spatial frequencies corresponding to the ticks on a given ruler. The approach presented leverages Deep Learning methods, specifically Mask Region proposal based Convolutional Neural Networks (R-CNN), for rulers’ recognition and segmentation, as well as several other computer vision (CV) methods such as adaptive thresholding and template matching. This paper describes a system for non-contact object measurement by sensing and assessing the distinct spatial frequency of the graduations on a ruler. When a ruler is placed in the same plane of an object being measured it can serve as metric reference, thus a measurement system can be defined and calibrated to correlate actual dimensions with pixels contained in an image. Determining an object measurement is a challenging task without having a well-defined reference.
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