摘 要
神經網絡模式識別方法是近幾年興起的模式識別領域的一個新的研究方向。由于神經網絡的高速并行處理、分布存貯信息等特性符合人類視覺系統的基本工作原則．具有很強自學習性 、組織性、容錯性 、高度非線性、高度魯棒性、聯想記憶功能和推理意識功能。本文首先介紹了神經網絡的基本概念，特別是BP神經網絡的原理和基本算法。同時介紹了模式識別的各種方法，并進一步了解了字符識別技術的基本理論。主要討論用人工神經網絡方法對英文字符的識別，將標準印刷體英文字符進行二值化處理，用矩陣表示，從而進行識別。同時考慮了噪聲干擾或者是非線性因素的存在。分別用理想樣本和混有噪聲的樣本訓練神經網絡并繪出誤差曲線進行比較。再者通過設置回調函數利用GUI界面開發字符識別系統，使其直觀顯示。
關鍵詞 神經網絡;字符識別
Abstract
Neural networks method is a new method in recent pattern recognition; it provides a new means to Character Recognition. It has the advantage over some traditional technology: Good tolerance, Classification capability, Parallel processing capacity and self-learning ability. Thus, the neural network identification is a good choice.
The passage introduces all kinds of methods of character recognition, mainly discuss using Artificial Neural Network method to recognize character, and consider the existence of noise disturbance or nonlinear factor, so the network has certain fault-tolerance capability, and finally use MATLAB to simulate character recognition.
The study focuses on the standard Print English character recognition. Union pattern recognition and artificial neural network algorithm for a large number of related, especially feed-forward neural network model which is the most useful in target identification at present and the BP algorithm, and making use of GUI platform to develop the character recognition system in the end.
