KVerifyID: A Hybrid Multimodal Approach for Khmer Online Writer Verification
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.