So I have an algorithm $A$ and an algorithm $B$ that is run on a set of $n$ documents. The documents are financial forms that consist of data that algorithm $A$/$B$ extracts. For each of the $n$ documents, algorithm $A$/$B$ is run and then these extractions are tested which results in an accuracy measured in %, i.e. $0 \leq Accuracy(Extraction_i) \leq 100$ where $1 \leq i \leq n$. This results in two arrays/vectors of accuracies, $Vec_A$ and $Vec_B$.
Let's say that the average accuracy for algorithm $A$ is $x$% higher than for $B$. What statistical test would I use in order to be able to claim that, with 95% confidence, algorithm $A$ performs better than $B$ for the chosen data set? (The same data set is used for both algorithms).
Thankful for any pointers, been a while now since I took a course in statistics.