Compare names and text with token-level Jaro-Winkler similarity scoring.
Users
2
−2 in 30 days
Rating
0.0 / 5
unchanged in 30 days
Ratings
0
0 in 30 days
Store rank
#367K
#278K among extensions
Version
1.0.0
updated 11 Aug 2026
Users
monthly, 2 months
Latest, 16 Oct 2026:2 usersMove the mouse over the chart for any month
Ratings count
monthly, 2 months
Latest, 16 Oct 2026:0 ratingsMove the mouse over the chart for any month
The Chrome Web Store rounds user counts down to the leading digit, so the user line moves in steps. The ratings count is exact and is the better signal of real adoption.
About SimCheck
SIMCHECK 是一款轻量级的名称与文本相似度对比工具。基于经典的 Jaro–Winkler 相似度算法,帮你快速判断两段名称或文本的接近程度,并给出一个直观的百分比得分。SIMCHECK is a lightweight name & text similarity checker. Powered by the classic Jaro–Winkler similarity algorithm, it instantly tells you how close two names or strings are, as an intuitive percentage score.
适用场景:
核对两个名字是否指向同一对象(人名、公司名、产品名);数据清洗时发现近似重复的记录;模糊比对拼写差异、大小写与多余空格;Great for: checking whether two names likely refer to the same person, company, or product; spotting near-duplicate records while cleaning data; fuzzy comparison across typos, casing, and extra spaces;
特点:
①相似度得分实时计算,结果一目了然;算法轻量,随手即用。real-time similarity scores at a glance; lightweight and ready when you are.
②完全本地运行,不上传任何数据。 Fully local — your data never leaves the browser.
逻辑:
1、把 source data 拆成多个 token。
2、把每条 target data 也拆成多个 token。
3、对 source 的每个 token,去 target 的所有 token 里找最相似的那个。
4、把每个 source token 的最佳相似度加总。
5、如果 target 比 source 多很多 token,会加一个长度惩罚。
6、最后得到百分比分数。
计算公式:
最终相似度 = source 每个 token 的最佳匹配分之和 / (source token 数 + 多余 target token 数 * 0.3),其中,多余 target token 数 = max(0, target token 数 - source token 数)
score = sumMax / (sourceLen + max(0, targetLen - sourceLen) * 0.3)
scorePercent = score * 100
GitHub:https://github.com/qiuhb2014/SIMCHECK