ABSTRACT The Optical Character Reader (OCR) process means the transition from scanned manual or written images to a machine-determined document. The American Standard Code for Information Interchange (ASCII) in cognitive processing uses OCR. The challenge is two primary folds: word segmentation by letters and character recognition. Implement a new approach to include the two functions by Scale-Invariant Transforming Feature (SIFT) descriptors. To compare SIFT descriptors (RootSIFT), devise a new procedure, that offers outstanding results without increasing computation or storage requirements. In order to identify English characters, Artificial Bee Colony (ABC) method suggests that the back propagation neural network for classification of character be utilized. Conducted experiments with more than 10 measures intended for every character and tested the accuracy for numerical numbers, chart letters, small letters and alphanumeric characters. The performance analysis of ABC optimized neural network algorithm has achieved a maximum accuracy of 97.3077% compared with precision recall and f-measure.

中文翻译：

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基于局部不变特征的光学字符识别

摘要 光学字符阅读器 (OCR) 过程意味着从扫描的手动或书面图像转换为机器确定的文档。认知处理中的美国信息交换标准代码 (ASCII) 使用 OCR。挑战有两个主要方面：字母分词和字符识别。实施一种新方法，通过尺度不变变换特征 (SIFT) 描述符包含这两个函数。要比较 SIFT 描述符 (RootSIFT)，请设计一个新程序，在不增加计算或存储要求的情况下提供出色的结果。为了识别英文字符，人工蜂群（ABC）方法建议利用反向传播神经网络进行字符分类。对每个字符进行了 10 多个措施的实验，并测试了数字、图表字母、小写字母和字母数字字符的准确性。ABC优化神经网络算法的性能分析，与精度召回和f-measure相比，达到了97.3077%的最高准确率。