String Comparison(字符串比较)研究综述
String Comparison 字符串比较 - That process of filtering on individuals or entities could be automated by using individualization algorithms, searching techniques based on string comparisons, artificial intelligence, and facial recognition. [1] Contrarily, string comparison in a digital document or cross-referencing entries (e. [2] The results of experiments demonstrate that our proposed method has better performance than string comparison and grammar tree analysis when fighting against variable substitution, insert independent statement and data stream confusion. [3] The above results open to the study of new applications of Lyndon words and inverse Lyndon words in the field of string comparison. [4] For the field-value extraction, a combination of rule-based keywords and navigation approach is used, utilising an Optical Character Recognition (OCR) for text extraction and regular expression for string comparison. [5] However, rather than using a string comparison or cosine similarity to calculate the distance between pair-wise fingerprint records, a binary number comparison function was used in DBSCAN. [6]通过使用个性化算法、基于字符串比较的搜索技术、人工智能和面部识别，可以自动过滤个人或实体。 [1] 相反，数字文档中的字符串比较或交叉引用条目（例如。 [2] 实验结果表明，在对抗变量替换、插入独立语句和数据流混淆时，我们提出的方法比字符串比较和语法树分析具有更好的性能。 [3] 上述结果为研究林登词和逆林登词在字符串比较领域的新应用打开了大门。 [4] 对于字段值提取，使用基于规则的关键字和导航方法的组合，利用光学字符识别（OCR）进行文本提取和正则表达式进行字符串比较。 [5] 然而，不是使用字符串比较或余弦相似度来计算成对指纹记录之间的距离，而是在 DBSCAN 中使用二进制数比较函数。 [6]
string comparison method
In the third step, the advertisements are detected using string comparison methods. [1] Experimental results shows that the proposed system requires only 16% commands to achieve the same level of performance when compared with the conventional string comparison method. [2] The Domain name similarity checker uses deep learning architecture and compared with the classical string comparison methods. [3] In this paper, we provide extensive experimental results over a number of popular string measures which indicate that string comparison methods fall short when applied to specific groups, a fact leading to algorithmic bias against these groups. [4] Gene annotation has traditionally required direct comparison of DNA sequences between an unknown gene and a database of known ones using string comparison methods. [5]第三步，使用字符串比较方法检测广告。 [1] 实验结果表明，与传统的字符串比较方法相比，所提出的系统只需要 16% 的命令即可达到相同水平的性能。 [2] 域名相似性检查器使用深度学习架构，并与经典的字符串比较方法进行比较。 [3] 在本文中，我们针对一些流行的字符串度量提供了广泛的实验结果，这些结果表明字符串比较方法在应用于特定组时存在不足，这一事实导致算法对这些组产生偏见。 [4] 基因注释传统上需要使用字符串比较方法直接比较未知基因和已知基因数据库之间的 DNA 序列。 [5]
string comparison technique
To address the problem mentioned above, existing works use simple approaches related to string comparison techniques that are extensively applied to compare genomes. [1] Most traditional ER studies identify records based on string-based data, so the ER problem relies mostly on string comparison techniques. [2]为了解决上述问题，现有工作使用与广泛应用于比较基因组的字符串比较技术相关的简单方法。 [1] 大多数传统的 ER 研究基于基于字符串的数据来识别记录，因此 ER 问题主要依赖于字符串比较技术。 [2]
string comparison algorithm 字符串比较算法
In Previous, research on comparing those two open source OCR engine, there we made comparison on basic factors which included speed, hardware requirements, accuracy ,but in that case, accuracy was been calculated manually which gave us results but with less precise, as it was a manual process to substitute scraped data to that formulas, In this research we’ve made results with more precision by performing a String comparison algorithm named, “Levenshtein Distance Algorithm” which is deployed in UiPath. [1] Current approaches typically revolve around string comparison algorithms like the Demaru-Levenschtein Distance (DLD) algorithm. [2]在之前的比较这两个开源 OCR 引擎的研究中，我们对包括速度、硬件要求、准确性在内的基本因素进行了比较，但是在这种情况下，准确性是手动计算的，这给了我们结果但精度较低，因为它是一个手动过程，将抓取的数据替换为该公式，在这项研究中，我们通过执行名为“Levenshtein Distance Algorithm”的字符串比较算法获得了更精确的结果，该算法部署在 UiPath 中。 [1] 当前的方法通常围绕字符串比较算法，例如 Demaru-Levenschtein 距离 (DLD) 算法。 [2]