This paper presents a novel Graph Convolutional Network (GCN) based framework for verifying OCR predictions on real Hindi document images, specifically addressing the challenges of complex conjuncts and character segmentation. Our approach first segments Hindi characters in real book images at different levels of granularity, while also synthetically generating word images from OCR predictions. Both real and synthetic images are processed through ResNet-50 to extract feature representations, which are then segmented using multiple patching strategies (uniform, akshara, random, and letter patches). The bounding boxes created using segmentation masks are scaled proportionally to the feature space while extracting features for GCN. We construct a line graph where each node represents a real-synthetic character pair (in feature space). Each node of the line graph captures semantic and geometric features including i) cross-entropy between original and synthetic features, ii) Hu moments difference for shape properties, and iii) and pixel count difference for size variation. The GCN with three convolutional layers (and ELU activation) processes these graph-structured features to verify the correctness of OCR predictions. Experimental evaluation on 1000 images from diverse Hindi books demonstrates the effectiveness of our graph-based verification approach in detecting OCR errors, particularly for challenging conjunct characters where traditional methods struggle.

While majority of current resources rely on formal text corrections, our work shifts the focus to non-native spoken Hindi error correction, which presents unique challenges due to its rich morphology, complex syntax, and distinct error patterns. To address the scarcity of authentic learner data, we introduce HiLearners, a dataset gathered from 2,500 real non-native Hindi speakers across three linguistic backgrounds (English, Bengali, Dravidian), capturing authentic error patterns including transfer errors, overgeneralization patterns, and contextual agreement issues. Furthermore, to overcome data resource limitations, we develop a methodical synthetic data augmentation technique, utilizing Large Language Models (LLMs) with a pattern analysis and controlled generation approach similar to Retrieval-Augmented Generation (RAG), yielding 5,500 carefully verified synthetic examples. Through extensive experiments on individual, mixed, and progressive curriculum-based configurations using multilingual models, we demonstrate that LLM-based synthetic data combined with three-phase curriculum learning significantly boosts performance, achieving a 76.92 GLEU score and surpassing human-only baselines. This work bridges the gap between native-centric error correction research and non-native Hindi learner needs, establishing a realistic assessment standard for advancing low-resource language processing.