Online writer verification with dynamic handwriting signals is still difficult, and it has been especially under-studied forcomplex Southeast Asian scripts like Khmer. This work tackles online, text-independent, word-level Khmer writer verification as apairwise decision problem: given two handwritten word samples, decide whether they were written by the same person. We introduceKVerifyID, a hybrid dual-stream Siamese network that learns from both (i) a grayscale image rendering of each word and (ii) itspen-trajectory sequence (𝑥, 𝑦, 𝑝)with explicit pen-state encoding. The resulting modality embeddings are fused into a compact 128-dimensional writer representation, and verification is performed via cosine similarity, using thresholds selected on validation andthen fixed for testing. On a Khmer online handwriting dataset collected from 298 writers (4,878 word instances) with strict writer-disjoint splits, the model generalizes strongly, achieving 99.50% training accuracy and 99.74% test accuracy, with a low verificationerror of 0.32% validation EER (equal error rate). At the validation equal-error operating point, the errors are FAR (false acceptancerate) = 0.30% and FRR (false rejection rate) = 0.19%. Overall, the results show that jointly leveraging spatial word appearance andonline stroke dynamics enables robust Khmer writer verification, making it promising for digital authentication and forensicscreening.
Kimlong Ngin, Dona Valy, Sokkhey Phauk et al.· Techno-Science Research Jour...· 0 citations
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.
Kimlong Ngin, Dona Valy, Kimhor Phoeurn et al.· Techno-Science Research Jour...· 0 citations
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