虚拟数据集识别亚述楔形文字

A. M. Rahma, Alipour Saeid, Muhsen J. Abdul Hussien
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引用次数: 4

摘要

楔形符号是模式识别中的一个复杂问题,特别是光学字符识别(OCR),这是由于与类楔形字符失真和字体异构相关的挑战。本文提出了用OCR对亚述楔形文字进行分类识别的新方法。作为一种通过处理复杂字符的符号来识别亚述字母的新方法。所使用的数据集由16个模式组成,以反映每个楔形文字符号与其形状和方向相关的所有概率,假设每个字符由一组符号组成。使用多边形逼近技术生成分类任务的特征向量。根据特征向量使用的算法不同,该方法可获得高达91%的分类率。
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Recognize assyrian cuneiform characters by virtual dataset
Cuneiform symbols represent a complex problem in pattern recognition, in particular for OCR (optical character recognition) due to challenges related to cuneiform-like character distortion and font heterogeneity. This paper proposes new approaches to recognise Assyrian cuneiform characters using OCR to classify the symbols. as a new way to recognize the Assyrian letters by dealing with symbols of complex character. The dataset utilised consists of 16 patterns to reflect all probabilities associated with each cuneiform symbol related to their shape and directions, assuming each character consists of a set of symbols. Polygon approximation techniques are used to generate feature vectors for the classification tasks. The proposed method obtains classification ratios up to 91% depending on the algorithm used for the feature vector.
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