RECIQ
Multimodal Receipt Extraction System
Real-world Tamil Nadu invoices are full of Tanglish — Tamil food names, merchant identifiers, and item descriptions written in Latin script that generic vision-language models simply don't understand. On top of that, the schema is extreme: 174 fields, but fewer than 7% filled per invoice. Standard models hallucinate values or produce unparseable output.
RECIQ tackles both problems on Qwen2-VL-7B-Instruct through three core ideas: tokenizer surgery that injects all 174 schema field names as atomic vocabulary tokens, LoRA fine-tuning that concentrates gradient signals on schema-relevant positions, and a grammar-constrained beam decoder that guarantees syntactically valid JSON at every generation step. A downstream GNN anomaly detector built on RECIQ's structured output achieves macro F1 of 70.0%, demonstrating real utility for financial auditing.