Automatic Speech Recognition and its applications are rising in popularity across applications with reasonable inference results. Recent state-of-the-art approaches, often employ significantly large-scale models to show high accuracy for ASR as a whole but often do not consider detailed analysis of performance across low-resource languages applications. In this preliminary work, we propose to revisit ASR in the context of Connected Number Recognition (CNR). More specifically, we (i) present a new dataset HCNR collected to understand various errors of ASR models for CNR, (ii) establish preliminary benchmark and baseline model for CNR, (iii) explore error mitigation strategies and their after-effects on CNR. In the due process, we also compare with end-to-end large scale ASR models for reference, to show its effectiveness.