With the increasing number of digitized historical documents, information processing has become a fundamental task to exploit the information contained in these documents. Thus, it is very significant to develop efficient tools in order to analyze and recognize them. One of these means is word spotting which has lately emerged as an active research area of historical document analysis. Various techniques have been suggested successfully to enhance the performance of word spotting systems. In this paper, an enhanced word spotting approach for historical Arabic documents is proposed. It involves improving learning feature representations that characterize word images. The proposed approach is mainly based on transfer learning. More precisely, it consists in building an embedding space for word image representations from an online training triplet-CNN, while performing transfer learning by leveraging the varied knowledge acquired from two different domains. The first domain is Hebrew handwritten documents, the second is English historical documents. We will investigate the impact of each domain in improving the representation of Arabic word images. As a final step, in order to evolve the word spotting system, the query word image along with all the reference word images will be projected into the embedding space where they will be matched according to their embedding vectors. We evaluate our method on the historical Arabic VML-HD dataset and show that our method outperforms significantly the state-of-the-art methods.