Why would 2 sets of similar training samples take significantly longer to train?
There could be multiple factors that could cause this to happen:


If the data types are different for the two images - np.float32 vs np.float64
Your computer might be performing some compression and decompression to optimize memory usage. If most of the pixels are very different from each other in the Arabic dataset, then the compressed size may be larger and require more time to decompress.
Your computer happened to have other competing processes running at the same time and distorted the execution results for one of your runs.


I would suggest you to use methods like timeit that do multiple runs to verify that this behaviour is consistent.