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An Enhanced Approach to Memetic Algorithm Used for Character Recognition

机译：用于字符识别的麦克算法的增强方法

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Character recognition is a best case to apply logics from Memetic Algorithms (MA) for image processing. In cases, like finger print matching, cent percent accuracy is expected but the character recognition on other hand can auto correct some errors. Time of processing is not the first criteria in figure print analysis but accuracy is a must, whereas while extracting characters from image speed of processing becomes more important parameter. This aspect of character recognition provides wide scope of implementing MA. The typing on QWERTY keyboard is the best example of brain using MA and dividing the character search in two parts with 13 characters for left hand and 13 for right. We never need to cross hands for typing next character as the design of keyboard is ensures that in most of the cases consecutive characters appear in specific sequence and brain keeps itself already prepared to hit next key but waits for confirmation. As we move dipper into string the search starts reducing which further enhances the predictive capacity of brain for expected next character. This can be seen as local search and cultural evolution which is performed by brain with each successive character in a string in [1,2]. Hence character recognition is more of string recognition than treating every character in isolation. This paper explains the above theory with results and also presents an enhanced MA for character recognition.
机译：字符识别是应用Memetic算法（MA）的最佳案例以进行图像处理。在手指打印匹配的情况下，预期的百分比精度，但在另一只手上的字符识别可以自动纠正一些错误。处理时间不是图形打印分析的第一个标准，但精度是必须的，而从图像速度从图像速度提取字符变得更重要的参数。字符识别的这个方面提供了广泛的实施mA。在QWERTY键盘上打字是使用MA的大脑的最佳示例，并将角色搜索分为两个部分，左手13个字符和13个字符。我们从不需要交叉双手键入下一个字符作为键盘的设计，确保在大多数情况下，在特定的序列中出现连续字符，大脑保持自身已准备好达到下一个键，但等待确认。当我们将Dipper移动到String时，搜索开始减少，这进一步增强了预期下一个字符的大脑的预测能力。这可以被视为本地搜索和文化演进，其由大脑与[1,2]中的字符串中的每个连续字符进行。因此，字符识别更像是字符串识别，而不是在隔离中处理每个角色。本文用结果解释了上述理论，并且还具有增强的MA，用于性格识别。

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