Layout-Aware Arabic Invoice Understanding with Product and Key-Value Extraction Using YOLOv11, AraBERT, and Active Learning

Abstract

Arabic invoice processing requires not only recognition of generic entities, but also interpretation of their invoice-specific roles and relations. This article extends a YOLOv11–AraBERT active-learning pipeline with a layout-aware structural layer for field classification, key-value linking, and product line-item reconstruction. YOLOv11 localizes text regions in a custom Arabic invoice dataset, OCR converts the detected regions to text, and AraBERT performs sequence labeling with an entity inventory that includes PRODUCT. Layout-AraBERT then combines AraBERT representations, predicted entity types, and normalized two-dimensional bounding-box features to distinguish fields such as invoice date, tax amount, subtotal, total amount, product name, quantity, unit price, and line total. On the authors’ custom dataset, YOLOv11 reaches 93.42% mAP@0.5:0.95 and the final AraBERT active-learning batch reaches 89.36% entity-level F1. PRODUCT extraction reaches 87.77% F1. For the structural stage, Layout-AraBERT reaches 91.2% field accuracy and 88.7% key-value linking F1 under clean OCR conditions; at a 20% synthetic OCR-noise level these values decrease to 81.6% and 78.2%, respectively. Evaluation also includes cross-template generalization, ablation analysis, and annotation-efficiency measurements. The resulting framework provides a reference baseline for layout-aware Arabic invoice understanding on the authors’ custom dataset.