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首页> 外文会议>2017 International Conference on Inventive Computing and Informatics >Intelligent tool for Malayalam cursive handwritten character recognition using artificial neural network and Hidden Markov Model
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Intelligent tool for Malayalam cursive handwritten character recognition using artificial neural network and Hidden Markov Model

机译：基于人工神经网络和隐马尔可夫模型的马拉雅拉姆草书手写字符识别智能工具

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This paper represents an attractive method for the conversion of the image into an editable text as it is written by Optical Character Recognition (OCR). With this offline handwritten character recognition method, it shows the ability of a computer to receive and recognize handwritten input. Computers may find some difficulty in deciphering the exact handwritten characters with different fonts and styles. This paper mainly focuses on the recognition of handwritten Malayalam (a South Indian Language) characters. Thus cursive Malayalam characters can be recognized by Hidden Markov Model (HMM). The classification is done with Artificial Neural Network (ANN). Handwritten character recognition with high accuracy and efficient method to recognize the cursive letters are included in the proposed system.
机译：本文提出了一种有吸引力的方法，可将图像转换为通过光学字符识别（OCR）编写的可编辑文本。使用这种离线手写字符识别方法，它显示了计算机接收和识别手写输入的能力。计算机在解密具有不同字体和样式的确切手写字符时可能会遇到一些困难。本文主要关注手写的马拉雅拉姆语（一种南印度语言）字符的识别。因此草书马拉雅拉姆语字符可以通过隐马尔可夫模型（HMM）识别。使用人工神经网络（ANN）进行分类。所提出的系统包括高精度的手写字符识别和识别草书字母的有效方法。

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