Recognizing handwritten digits is a fundamental aspect of optical character recognition (OCR), with broad applications in areas such as digital archiving, data entry, and assistive technologies. Khmer digits present distinctive challenges due to their intricate shapes and high variability in individual handwriting styles, making the development of accurate recognition systems particularly demanding. Unlike conventional approaches that primarily rely on image-based inputs, this study adopts a sequential framework using coordinate data, which captures the temporal dynamics of pen strokes and preserves the natural writing sequence. This aspect is especially critical for Khmer digits, where stroke order follows well-defined structural rules. Three recurrent neural network architectures such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) were evaluated, with data augmentation applied to improve robustness. A custom dataset of 1,583 handwritten sequences was expanded to 12,337 samples through rotation-based augmentation, partitioned into 60% training, 20% validation, and 20% testing. The experimental evaluation reported test accuracies of 95.27% for LSTM, 94.95% for Bi-LSTM, and 95.58% for GRU, with GRU showing the most promising results. These outcomes validate the effectiveness of sequential modeling for Khmer digit recognition and emphasize the value of RNN-based methods for complex handwriting systems.