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This paper presents a two-phased stroke-like pattern noise (SPN) removal algorithm for binary document images. The proposed approach aims at understanding script-independent prominent text component features using supervised classification as a first step. It then uses their cohesiveness and stroke-width properties to filter and associate smaller text components with them using an unsupervised classification technique. In order to perform text extraction, and hence noise removal, at diacritic-level, this divide-and-conquer technique does not assume the availability of accurate and large amounts of ground-truth data at component-level for training purposes. The method was tested on a collection of degraded and noisy, machine-printed and handwritten binary Arabic text documents. Results show pixel-level precision and recall of 86% and 90% respectively for noise-pixels.