OCR and multimodal LLM pipelines are moving from novelty to daily tools. In OCR debates, Gpt-4o and Gemini 2.5 Pro are common baselines, while Qwen2.5-VL and LLaVA are used for integrated OCR plus structured extraction [1].
Two practical stacks are turning heads:
• PaddleOCR-VL 0.9B – production-ready for multi-column PDFs, tables, and formulas; on-device inference keeps costs down [2].
• Two-stage workflow: crop with PP-Structure or a tiny detector, deskew/upscale with Real-ESRGAN-lite, then OCR with PaddleOCR and map keys to a JSON schema; Qwen2.5-VL-7B provides a strict JSON fallback for tough shots [1].
• For reliability, pull UPCs from Open Food Facts and only overwrite missing fields to preserve serving sizes [1].
• Batch pipelines can slot in docupipe.ai alongside Qwen2.5-VL for schema-first JSON [1].
DeepSeek OCR shines with Contexts Optical Compression and is often weighed against Qwen3-VL as a general-purpose VL option [3].
Many top models lean toward image-text-to-text: PaddleOCR-VL, DeepSeek-OCR, Nanonets-OCR2-3B, Qwen3-VL are cited; DeepSeek-OCR even dropped its own model, signaling a shift to multimodal reasoning beyond chat [4]. VL models are handy for screen snippets and UI problems too.
Watch for broader adoption in on-device OCR, multilingual layouts, and document parsing beyond chat.
References
Best Model for OCR
OCR-focused LLMs and pipelines; multimodal models, prompts, JSON schema, and fallbacks for nutrition labels.
View sourcePractical takeaways from recent hands-on use of PaddleOCR‑VL 0.9B
PaddleOCR-VL praised for structure and edge-case handling; GPT-4o/Gemini favored for chat; tradeoffs include cost, privacy, and one-pass pipeline.
View sourceDeepSeek releases DeepSeek OCR
Discusses DeepSeek OCR, context compression, comparisons to Qwen3/VL, running LLMs locally, frameworks, data, and benchmarks for OCR open AI research
View sourceAre Image-Text-to-Text models becoming the next big AI?
Notes rise of image-text-to-text LLMs, praise layout and handwriting understanding, mentions ChatGPT usage with screenshots, questions industry push.
View source