Is the graphic of deep residual networks wrong?
(Per the diagram), $F(x)$ here is simply the entire two-layer non-linear chain that is operating on the input $x$. Then, the final output is simply $F(x) + x = H(x)$. That's it! 

The thing that may be confusing you is that $F(.)$. In this case, they do not mean for $F$ to simply encompass one operation. Instead, it encompasses any set of operations processing $x$, up until you add $x$ back. Hope that helps! 

PS: It is also common to see this type of nomenclature in a lot of DNN literature, whereby one refers to an entire deep non-linear chain as $D(x)$. For example in Generative Adversarial Networks, (GAN)s, $D(x)$ refers to the entire deep net devoted to the discrimination process, while $G(x)$ refers to the entire net devoted to the noise shaping. In both cases, they are composed of entire functions/nets, and do not signify simply one operation.