联机变形德文汉字识别的联想记忆模型

Gaurav Pagare, K. Verma
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引用次数: 5

摘要

在今天的场景中,机器和人类的交互是非常重要的。这种交互将使搜索引擎、社交媒体、人工智能、认知计算更具互动性和用户友好性。手写识别是对手写文件中的文字、数字和符号进行识别的系统过程。在目前的工作中,提出了一种数字化手写德文汉字的识别模型。本文提出了一种基于分类器的梵文字符和数字自动联想识别技术。为了解决识别问题,采用了基于Hopfield神经网络的动态模型。该模型采用并行运算,求解识别问题的速度更快,性能更优。
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Associative Memory Model for Distorted On-Line Devanagari Character Recognition
Machine and human interaction is very essential in today's scenario. This interaction would make search engines, social media, artificial intelligence, cognitive computing more interactive and user friendly. Handwriting recognition is the systematic process of identifying the characters, numbers and symbols present in the handwritten document. In the current work, a recognition model for digitizing handwritten Devanagari characters proposed. Auto associative recognition technique for Devanagari characters and numerals proposed in the current work by using classifiers. To solve recognition problem a dynamic model based on Hopfield neural network deployed. The model performs operation in parallel making it faster and optimal in solving recognition problem.
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