While deep learning has achieved great success in building vision applications for mainstream users, there is relatively less work for the blind and visually impaired to have a personal, on-device visual assistant for their daily life. Unlike mainstream applications, vision system for the blind must be robust, reliable and safe-to-use. In this paper, we propose a fine-grained currency recognizer based on CONGAS, which significantly surpasses other popular local features by a large margin. In addition, we introduce an effective and light-weight coarse classifier that gates the fine-grained recognizer on resource-constrained mobile devices. The coarse-to-fine approach is orchestrated to provide an extensible mobile-vision architecture, that demonstrates how the benefits of coordinating deep learning and local feature based methods can help in resolving a challenging problem for the blind and visually impaired. The proposed system runs in real-time with ~150ms latency on a Pixel device, and achieved 98% precision and 97% recall on a challenging evaluation set.