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首页> 外文期刊>International Journal of Image Processing >Faster Training Algorithms in Neural Network Based Approach For Handwritten Text Recognition
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Faster Training Algorithms in Neural Network Based Approach For Handwritten Text Recognition

机译：基于神经网络的手写文本识别方法中的更快训练算法

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Handwritten text and character recognition is a challenging task compared to recognition of handwritten numeral and computer printed text due to its large variety in nature. As practical pattern recognition problems uses bulk data and there is a one step self sufficient deterministic theory to resolve recognition problems by calculating inverse of Hessian Matrix and multiplication the inverse matrix it with first order local gradient vector. But in practical cases when neural network is large the inversing operation of the Hessian Matrix is not manageable and another condition must be satisfied the Hessian Matrix must be positive definite which may not be satishfied. In these cases some repetitive recursive models are taken. In several research work in past decade it was experienced that Neural Network based approach provides most reliable performance in handwritten character and text recognition but recognition performance depends upon some important factors like no of training samples, reliable features and no of features per character, training time, variety of handwriting etc. Important features from different types of handwriting are collected and are fed to the neural network for training. It is true that more no of features increases test efficiency but it takes longer time to converge the error curve. To reduce this training time effectively proper train algorithm should be chosen so that the system provides best train and test efficiency in least possible time that is to provide the system fastest intelligence. We have used several second order conjugate gradient algorithms for training of neural network. We have found that Scaled Conjugate Gradient Algorithm , a second order training algorithm as the fastest for training of neural network for our application. Training using SCG takes minimum time with excellent test efficiency. A scanned handwritten text is taken as input and character level segmentation is done. Some important and reliable features from each character are extracted and used as input to a neural network for training. When the error level reaches into a satisfactory level (10 -12 ) weights are accepted for testing a test script. Finally a lexicon matching algorithm solves the minor misclassification problems.
机译：与手写数字和计算机打印文本的识别相比，手写文本和字符的识别由于其种类繁多而具有挑战性。由于实际的模式识别问题使用大量数据，因此存在一步自身的确定性理论，可以通过计算Hessian矩阵的逆并将逆矩阵与一阶局部梯度矢量相乘来解决识别问题。但是在实际情况下，当神经网络很大时，Hessian矩阵的逆运算是无法控制的，必须满足其他条件，Hessian矩阵必须是正定的，可能无法满足。在这些情况下，将采用一些重复的递归模型。在过去十年的几项研究工作中，都经历了基于神经网络的方法在手写字符和文本识别方面提供最可靠的性能，但是识别性能取决于一些重要因素，例如训练样本数，可靠特征和每个字符的特征，训练时间，各种笔迹等。收集来自不同笔迹类型的重要特征，并将其馈送到神经网络中进行训练。的确，没有更多的功能可以提高测试效率，但是收敛误差曲线需要更长的时间。为了有效减少训练时间，应选择适当的训练算法，以便系统在最短的时间内提供最佳的训练和测试效率，从而为系统提供最快的智能。我们已经使用了几种二阶共轭梯度算法来训练神经网络。我们发现缩放共轭梯度算法是我们的​​应用中作为神经网络训练最快的二阶训练算法。使用SCG进行培训需要最少的时间，并且测试效率极高。将扫描的手写文本作为输入并完成字符级别的分割。从每个角色中提取一些重要且可靠的特征，并将其用作神经网络进行训练的输入。当错误级别达到令人满意的级别（10 -12）时，可以接受权重来测试测试脚本。最后，词典匹配算法解决了较小的分类错误问题。

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