docext

Hey, the reason for the long processing time is that lots of people are using it, and with probably larger documents. I tested your file locally seems to be working correctly. https://ibb.co/C36RRjYs Regarding the token limit, it depends on the text. We are using the qwen-2.5-vl tokenizer in case you are interested in reading about it. You can run it very easily in a Colab notebook. This should be faster than the demo https://github.com/NanoNets/docext/blob/main/PDF2MD_README.m... There are incorrect words in the extraction, so I would suggest you to wait for the handwritten text model's release.

Correct link for Docext: https://github.com/NanoNets/docext/blob/main/PDF2MD_README.m...

Full disclaimer: I work at Nanonets Excited to share Nanonets-OCR-s, a powerful and lightweight (3B) VLM model that converts documents into clean, structured Markdown. This model is trained to understand document structure and content context (like tables, equations, images, plots, watermarks, checkboxes, etc.). Key Features: LaTeX Equation Recognition Converts inline and block-level math into properly formatted LaTeX, distinguishing between $...$ and $$...$$. Image Descriptions for LLMs Describes embedded images using structured <img> tags. Handles logos, charts, plots, and so on. Signature Detection & Isolation Finds and tags signatures in scanned documents, outputting them in <signature> blocks. Watermark Extraction Extracts watermark text and stores it within <watermark> tag for traceability. Smart Checkbox & Radio Button Handling Con

Excited to share Nanonets-OCR-s, a powerful and lightweight (3B) VLM model that converts documents into clean, structured Markdown. This model is trained to understand document structure and content context (like tables, equations, images, plots, watermarks, checkboxes, etc.). Key Features: LaTeX Equation Recognition Converts inline and block-level math into properly formatted LaTeX, distinguishing between $...$ and $$...$$. Image Descriptions for LLMs Describes embedded images using structured <img> tags. Handles logos, charts, plots, and so on. Signature Detection & Isolation Finds and tags signatures in scanned documents, outputting them in <signature> blocks. Watermark Extraction Extracts watermark text and stores it within <watermark> tag for traceability. Smart Checkbox & Radio Button Handling Converts checkboxes to Unicode symbols

The traditional pipeline for unstructured data extraction typically follows these steps: 1. Image → OCR Model (e.g., Google Vision) → Layout Model (e.g. Surya) → LLM → Final Answer However, this can be streamlined using a Vision-Language Model (VLM): 2. Image → VLM → Final Answer Recently VLMs have improved a lot for OCR and document understanding tasks, specifically the Qwen-2.5-VL series. We can run the Qwen-2.5-VL-7B-AWQ model locally with just 16GB VRAM, and perform end-to-end information extraction (fields and table extraction) without any external models. Hallucination with VLMs One question I am asked often is, higher hallucination with VLMs compared to OCR model. This is a valid point. But, even with correct OCR and Layout formatting, LLM can still hallucinate, and can give incorrect final answers. Layout models often struggle wit

The traditional pipeline for unstructured data extraction typically follows these steps: 1. Image → OCR Model (e.g., Google Vision) → Layout Model (e.g. Surya) → LLM → Final Answer However, this can be streamlined using a Vision-Language Model (VLM): 2. Image → VLM → Final Answer Recently VLMs have improved a lot for OCR and document understanding tasks, specifically the Qwen-2.5-VL series. We can run the Qwen-2.5-VL-7B-AWQ model locally with just 16GB VRAM, and perform end-to-end information extraction (fields and table extraction) without any external models. Hallucination with VLMs One question I am asked often is hallucination with VLMs compared to OCR model. This is a valid point. But, even with correct OCR and Layout formatting, LLM can still hallucinate, and can give incorrect final answers. Layout models often struggle with comple