Recognizing Khmer Handwritten Digits with the Power of Sequential RNNs
TL;DR
Three recurrent neuralnetwork architectures such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU) were evaluated, with GRU showing the most promising results.
Abstract
Recognizing handwritten digits is a fundamental aspect of optical character recognition (OCR), with broad applicationsin areas such as digital archiving, data entry, and assistive technologies. Khmer digits present distinctive challenges due to theirintricate shapes and high variability in individual handwriting styles, making the development of accurate recognition systemsparticularly demanding. Unlike conventional approaches that primarily rely on image-based inputs, this study adopts a sequentialframework 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 neuralnetwork 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 wasexpanded 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 GRUshowing the most promising results. These outcomes validate the effectiveness of sequential modeling for Khmer digit recognitionand emphasize the value of RNN-based methods for complex handwriting systems.