تنفيذ الجزء العملي في المقترحsystem Handwritten ten digit recognition

اريد تنفيذ الجزء العملي في المقترح

The project aims to design and build a software system to recognize handwritten digit using The method uses two consecutive attribute extractors. The first one generates the projected patterns on principal components or eigenvectors obtained from the covariance matrix of the data via PCA. The second provides the hidden layer outputs of a partially trained neural network (PTNN), where training of the neural network is stopped after a few epochs, i.e., the training is not fully completed. These two generators are cascaded, that is, the outputs of the PCA stage become the inputs of PTNN.

that the proposed feature generator reduces within-cluster variability. This makes it much easier to distinguish data from different classes. The original input data are first transformed into a new space referred to as the PCA feature space. Then, the feature space is transformed into another space through a PTNN with one hidden layer with various hidden units.

here that the framework proposed in this study reduces within-cluster variability as compared to state-of-the-art studies. In this way, it becomes much easier to differentiate data from different classes using the framework. Moreover, the proposed framework can enable achieving low intra-class and high inter-class variations.

والذي يتضمن

واجهة ادخال للحروف المكتوبة بخط اليد

برمجة خوارزمية pca

والخلية العصبية المؤقتة الجزئية ptnn

وخوارزمياتsvm , أو mdc

مع تطوير شيء من السستم لزيادة الدقة

نافذة اخراج تعطيالرقم الصحيح ونسبة الدقة