Memory Impulse Neural Networks for Text Recognition

Current text recognition tasks rely on evolutionary algorithms based on Convolutional Neural Networks (CNN), which achieve good accuracy in the task, but it relies on large datasets and has high latency. In this paper, a complete amnesia based impulse neural network (SNN) model is constructed and the STDP unsupervised learning algorithm is taken for training thereby performing the text recognition work. Using this scheme, this paper incorporates the nonlinear properties of the memristor and constructs a LIF model that is more consistent with the pulse output of a real biological neuron. Based on this, an STDP learning network based on the synaptic properties of amnesia-like synapses is constructed and trained, and the introduction of amnesia can save storage space and power consumption as well as improve real-time performance. This scheme can accurately and quickly identify the sample text through simulation, and the research scheme in this paper has inspirational effects for number recognition, pattern recognition, etc.