# Structured Data Extraction

A visual OCR model fine-tuned based on Qwen2.5-VL-3B-Instruct, focusing on document-level OCR, long-context visual language understanding, and mathematical LaTeX format conversion.

Qwen2.5-VL is the latest vision-language model from the Qwen family, featuring powerful visual understanding and multimodal processing capabilities, supporting image and video analysis with structured output.

Qwen2.5-VL is the latest vision-language model in the Qwen family, featuring powerful visual understanding and multimodal processing capabilities.

docscopeOCR-7B-050425-exp is a model fine-tuned based on Qwen/Qwen2.5-VL-7B-Instruct, focusing on document-level OCR, long-context visual language understanding, and accurate image-to-text conversion of mathematical LaTeX formats.

Qwen2.5-VL is the latest vision-language model in the Qwen family, focusing on enhanced visual understanding and agent capabilities.

Qwen2.5-VL is the latest vision-language model in the Qwen family, featuring powerful visual understanding and agent capabilities, supporting multi-format visual localization and structured output generation.

Qwen2.5-VL is a multimodal large language model launched by the QwenLM team, featuring powerful visual understanding and intelligent agent capabilities, supporting various input formats including images, videos, and text.

Qwen2.5-VL is the latest vision-language model in the Qwen family, featuring enhanced visual understanding, agent capabilities, and structured output generation.

Qwen2.5-VL is a multimodal vision-language model launched by Tongyi Qianwen, featuring powerful image understanding and text generation capabilities.

Qwen2.5-VL is the latest vision-language model in the Qwen family, featuring enhanced visual understanding, agent capabilities, and long video processing.

A financial table question answering model based on the LayoutLM architecture, specifically designed to extract and answer structured questions from financial tables.

A document understanding model fine-tuned based on microsoft/layoutlmv3-base, excelling in document layout analysis tasks

A table detection model based on DETR architecture, specifically designed to identify table regions in documents

This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased on the generator dataset, suitable for document understanding and layout analysis tasks.

A model fine-tuned based on naver-clova-ix/donut-base, potentially used for receipt recognition or document understanding tasks

CORD-v2 is a model for image-to-text tasks, primarily used for extracting and recognizing text content from images.

Document image understanding model fine-tuned based on naver-clova-ix/donut-base-finetuned-cord-v2

Donut is a visual document understanding model based on Swin Transformer, specifically fine-tuned for the CORD dataset, capable of extracting structured text information from images.

A table detection model based on DETR architecture, specifically designed to identify and extract tables from unstructured documents

A model fine-tuned on an image folder dataset based on naver-clova-ix/donut-base, with no specific use case explicitly stated

A model fine-tuned based on naver-clova-ix/donut-base, specific uses and functions require more information

Receipt recognition model fine-tuned based on naver-clova-ix/donut-base

A model fine-tuned based on philschmid/donut-base-sroie, suitable for image processing tasks

A table detection model based on YOLOv8, capable of identifying table regions in documents, supporting both bordered and borderless table types.

This model is a fine-tuned version of naver-clova-ix/donut-base on an image folder dataset, suitable for document understanding tasks.

A document understanding model fine-tuned based on philschmid/donut-base-sroie

A model based on the Donut architecture, specifically designed for extracting structured data from medical handwritten documents

Table Transformer is a table detection model based on the DETR architecture, specifically designed to detect and recognize table structures from document images.

A document understanding model fine-tuned from naver-clova-ix/donut-base, suitable for image text extraction tasks

A document understanding model fine-tuned on the CORD dataset based on LayoutLMv3, excelling in document token classification tasks

A document understanding model fine-tuned from microsoft/layoutlmv2-base-uncased, suitable for structured document information extraction tasks

A document information extraction model fine-tuned on the SROIE dataset based on the LayoutLMv2 architecture, excelling at extracting key fields from receipt documents