The Implementation of Artificial Neural Network (ANN) on Offline Cursive Handwriting Image Recognition

F. Fitrianingsih, Diana Tri Susetianingtias, Dody Pernadi, Eka Patriya, Rini Arianty
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引用次数: 2

Abstract

Data produced Segmentation was done using and contours. The modeling was carried out using and hard. The recognition model used the The results of the study are to be to Abstract Identifying a writing is an easy thing to do for human, but this does not apply to computers, in particular if it is handwriting. Handwriting recognition, especially cursive handwriting is a research in the area of image processing and pattern matching that is challenging to complete, following the different characteristics of each person's cursive handwriting style. In this study, the use of the ANN model will be implemented in performing offline handwriting image recognition. The cursive handwriting image that has been obtained is then preprocessed and segmented using bounding box rectangle and contour techniques. Evaluation of system performance using global performance metrics in this study resulted in a percentage of 93% where the
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人工神经网络在脱机手写体图像识别中的实现
生成的数据使用和轮廓进行分割。采用硬、硬两种方法进行建模。摘要识别笔迹对人类来说是一件容易的事情,但这并不适用于计算机,尤其是手写笔迹。手写识别,尤其是草书手写,是一项具有挑战性的图像处理和模式匹配领域的研究,因为每个人的草书手写风格都有不同的特点。在本研究中,将使用人工神经网络模型进行离线手写图像识别。然后使用边界框矩形和轮廓技术对得到的草书手写图像进行预处理和分割。在本研究中,使用全局性能指标对系统性能进行评估的结果是,93%的百分比
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