Writer identification remains a critical challenge in document analysis, necessitating robust methods to discern unique handwriting characteristics. This paper introduces a novel approach for writer identification by analyzing the distribution of oriented angles of pixels situated at a fixed distance R from each point in a handwriting image. The proposed method begins by computing the angle between vertical and horizontal pixel pairs located at distance R from every image point. These angles are quantized into discrete bins to form a local angular distribution. Subsequently, FAST keypoints are detected in the original handwriting image, and small fragments centered on these keypoints are extracted from the angular distribution map. Each fragment is flattened and encoded into a compact feature vector using the Vector of Locally Aggregated Descriptors (VLAD) method, capturing both local and global structural patterns. A k-nearest neighbors (KNN) classifier is employed for writer identification based on the aggregated feature representations. The method is rigorously evaluated on three benchmark datasets: BFL (Portuguese), CERUG-CH (Chinese), and CERUG-EN (English), encompassing diverse scripts and writing styles. Experimental results demonstrate the robustness and efficacy of the proposed approach, achieving competitive accuracy rates across all datasets. Specifically, the method achieves 99.05% on CERUG-EN, 99.05% on CERUG-CH, and 100% on BFL, confirming its effectiveness across different languages and writing conditions.