Ghost Handwritten Digit Recognition based on Deep Learning

在孟加拉手写数字识别中使用基于CNN的预训练模型的高效方法

Efficient approach of using CNN based pretrained model in Bangla handwritten digit recognition

针对孟加拉手写数字识别准确率低的问题，提出基于CNN的预训练模型（Resnet-50、Inception-v3和EfficientNetB0），在NumtaDB数据集上达到97%的准确率，超越现有方法。

Recognition of handwritten MNIST digits on low-memory 2 Kb RAM Arduino board using LogNNet reservoir neural network

The presented compact algorithm for recognizing handwritten digits of the MNIST database, created on the LogNNet reservoir neural network, reaches the recognition accuracy of 82%. The algorithm was tested on a low-memory Arduino board with 2 Kb static RAM low-power microcontroller. The dependences of the accuracy and time of image recognition on the number of neurons in the reservoir have been investigated. The memory allocation demonstrates that the algorithm stores all the necessary information in RAM without using additional data storage, and operates with original images without preliminar…

Reinforcement Learning Based Handwritten Digit Recognition with Two-State Q-Learning

We present a simple yet efficient Hybrid Classifier based on Deep Learning and Reinforcement Learning. Q-Learning is used with two Q-states and four actions. Conventional techniques use feature maps extracted from Convolutional Neural Networks (CNNs) and include them in the Qstates along with past history. This leads to difficulties with these approaches as the number of states is very large number due to high dimensions of the feature maps. Since our method uses only two Q-states it is simple and has much lesser number of parameters to optimize and also thus has a straightforward reward funct…