TL;DR Retail banking involves a wide range of document-heavy processes, including loan applications, credit card approvals, customer onboarding, and compliance checks. Traditional OCR and rule-based systems often fall short when processing these documents due to...
TL;DR We ran on the DocVQA validation split and got 5,286 correct out of 5,331 (99.15%). Of those 45 wrong answers, only 18 are true parsing shortcomings. DocVQA is usually used to evaluate vision-language models, but we are pioneering the use of this popular dataset...
TL;DR Signatures, stamps, and seals are critical markers of authenticity in documents. Yet detecting these elements remains difficult because they vary widely in form, placement, and quality. Traditional OCR and template-based systems often fail to capture them...
In the era of large language models, a silent barrier has emerged for non-English speakers. Despite the revolutionary capabilities of modern AI, there’s an uncomfortable truth: most mainstream models are trained predominantly on English corpora, creating an...
Nearly every enterprise runs on documents. Legal agreements, claims packets, forms, loan applications, contracts, just to name a few. These documents can be long, messy, complex, and multimodal, including scanned forms, nested/complex tables, charts, graphs,...
