Why Transformers are Winning the Document Wars

Document type classification isn't just about the text anymore. These days, it's a cocktail of text, images, and layout. Transformers are proving they're the best at mixing this drink.

The Multimodal Maze

visually rich documents, the challenge is piecing together a puzzle with parts scattered across different formats. Think text, visuals, and the way it all lays out on a page.

Research has shown that handling this mess, specialized multimodal transformers are cleaning up. They've outperformed their Large Language Model (LLM) cousins on the RVL-CDIP benchmark. Why? Because they know how to party with images and layouts like no other.

The OCR Dilemma

If you're relying on Optical Character Recognition (OCR) for text, you're playing with a handicap. Sure, it helps. But the real MVP is image data. That’s what’s giving transformers the edge in these complex documents. It’s like OCR is bringing a knife to a gunfight.

So, is it time to ditch OCR? Not entirely. It still has its place. But if you’re not prioritizing image and layout data, you're missing out on the real action.

What This Means for You

For those designing systems for document classification, the message is clear: multimodal transformers are worth the investment. They’re not just a trend. They're the future because they actually work. The old methods? They’re fading into the past.

Do you really want to gamble on outdated tech? The choice seems pretty clear. As far as I'm concerned, if you're not onboard with transformers, you're stuck in the dark ages. Show me the product that beats this, and maybe I'll change my tune.

For now, the message is simple. If you want the best document type classification, go multimodal. The proof is in the retention numbers, and transformers are leading the pack.