# OCR Enhancement

This is a 700M-parameter Vision Transformer model trained on 2 billion web images using masked autoencoder self-supervised learning, without language supervision.

A Turkish visual-language model specifically designed for multimodal visual instruction-following tasks, capable of processing both visual (image) and text inputs to understand and execute instructions provided in Turkish.

Idefics3 is an open-source multimodal model capable of processing arbitrary sequences of image and text inputs to generate text outputs. It shows significant improvements in OCR, document understanding, and visual reasoning.

A table structure recognition model developed based on Microsoft's Table Transformer for table detection and recognition tasks in documents

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

A document Q&A model fine-tuned based on microsoft/layoutlmv2-base-uncased. The specific training dataset is unknown.

CogAgent is an open-source vision-language model based on CogVLM, focusing on single-round visual question answering tasks

CogAgent is an open-source vision-language model based on CogVLM improvements, featuring GUI agent capabilities, multi-round visual dialogue, and visual grounding.

A document visual question answering model based on the LayoutLMv2 architecture, fine-tuned for DocVQA tasks

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

Model fine-tuned based on naver-clova-ix/donut-base, specific purpose not explicitly stated

A document visual question answering model based on the LayoutLMv2 architecture, fine-tuned specifically for document understanding tasks

A document visual question answering model based on the LayoutLMv2 architecture, specifically fine-tuned for document understanding tasks

A document understanding model fine-tuned from naver-clova-ix/donut-base, specialized in structured document information extraction tasks

A document understanding model fine-tuned from naver-clova-ix/donut-base, suitable for image folder datasets

This model is a fine-tuned version of microsoft/layoutlmv2-base-uncased for document visual question answering tasks, focusing on processing text and layout information in document images.

A model fine-tuned on the image folder dataset based on naver-clova-ix/donut-base, suitable for document understanding tasks

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

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

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

A document visual question answering model based on the LayoutLMv2 architecture, fine-tuned for document understanding tasks

Document understanding model based on the LayoutLMv2 architecture, fine-tuned for InfoVQA tasks

This is a LayoutLM model fine-tuned on the FUNSD dataset, specifically designed for document/form tag classification tasks.