This is an optimized version based on the original DeepSeek-OCR model, specifically an OCR model that supports inference on Apple Metal Performance Shaders (MPS) and CPU. It can extract text from images and convert it into a structured format, supporting multilingual document recognition.

MultimodalTransformersOther

This is an image text recognition model based on DeepSeek-OCR, which specifically solves the compatibility issues in the latest version of the transformers library, enabling the model to run smoothly in the latest versions such as transformers v4.57.1.

MultimodalTransformersEnglish

A quantized version of LightOnOCR-1B-1025, specifically designed for image-to-text tasks and widely used in fields such as document understanding and visual language processing. This model supports multiple European languages and is suitable for scenarios such as OCR, PDF processing, and table recognition.

MultimodalGgufMultiple Languages

DeepSeek-OCR-8bit is an MLX format version converted from the DeepSeek-OCR model. It is a visual language model specifically optimized for Apple chips, supporting multilingual OCR recognition and image text understanding tasks.

MultimodalMlxOther

DeepSeek-OCR-6bit is an MLX format version converted from the DeepSeek-OCR model, specifically optimized for Apple chips. This is a vision-language model with powerful optical character recognition capabilities, capable of extracting and recognizing text information from images.

MultimodalMlxOther

This model is a vision-language model in MLX format converted from DeepSeek-OCR, specifically designed for optical character recognition (OCR) tasks. It supports multilingual text recognition and image understanding.

MultimodalMlxOther

This is an MLX format model converted from the DeepSeek-OCR model, supporting multilingual image text recognition and text generation functions, specifically optimized for OCR tasks.

MultimodalMlxOther

Nanonets-OCR2-1.5B-exp GGUF is a powerful image-to-markdown OCR model that can convert documents into a structured markdown format and perform intelligent content recognition and semantic tagging. It supports the processing of multi-language documents.

MultimodalTransformersOther

This is a repaired image text-to-text model that solves the problem of inference failure of the original model in the latest Transformers version. This model is specifically designed for image recognition and text generation tasks and supports multimodal input processing.

MultimodalTransformersEnglish

olmOCR-2-7B-1025 is a high-quality OCR vision-language model developed by AllenAI, specifically designed to handle text recognition tasks in documents and images. This repository provides its GGUF quantized version, using the Q8_0 quantization method, which reduces the model size while maintaining excellent accuracy.

MultimodalGguf

The Nanonets-OCR2-3B GGUF model is a powerful tool designed for document processing. It can intelligently convert various types of documents into structured Markdown format and has multiple advanced recognition and processing capabilities such as OCR, image-to-text conversion, PDF-to-Markdown conversion, and visual question answering.

MultimodalTransformersOther

Chandra is an advanced OCR model that can extract text from images and PDFs with high precision and preserve layout information. It supports output in Markdown, HTML, and JSON formats and performs excellently in handwriting recognition, form reconstruction, table processing, etc. It supports more than 40 languages.

MultimodalTransformers

PaddleOCR-VL-0.9B is a vision-language model developed based on the PaddlePaddle framework, specifically designed for the task of converting image text to text. This model is a reproduction of the official PaddlePaddle version and supports the extraction and recognition of text content from images.

MultimodalTransformers

DeepSeek-OCR is an advanced optical character recognition model developed by DeepSeek AI, focusing on exploring the boundaries of visual text compression and capable of efficiently extracting and converting text content from images.

MultimodalSafetensorsOther

The Ming-flash-omni preview version is a multimodal large model built on the Ling-Flash-2.0 sparse mixture of experts (MoE) architecture, with a total of 100B parameters and only 6B parameters activated per token. This model is a comprehensive upgrade based on Ming-Omni, showing significant improvements in multimodal understanding and generation, especially in speech recognition, image generation, and segmentation editing.

MultimodalDiffusersEnglish

Persian OCR is a deep learning model for optical character recognition specifically designed for Persian text. It uses the CNN + Transformer architecture and is trained on a dataset containing 600,000 synthetic Persian text images, achieving a sequence accuracy of 96%.

Computer VisionPytorchOther

This is an FP8 quantized version of the OCR model fine-tuned based on Qwen2.5-VL-7B-Instruct, specifically designed for document image text recognition and supporting efficient large-scale document processing.

MultimodalTransformersEnglish

Perseus-Doc-vl-0712 is a multilingual vision-language model suitable for tasks such as text generation, image caption generation, and optical character recognition.

MultimodalTransformersMultiple Languages

Perseus-Doc-vl-0712 is a vision-language model that supports English and Chinese, suitable for various tasks such as text generation inference, image caption generation, and optical character recognition.

MultimodalTransformersMultiple Languages

The Perseus-Doc-vl-0712 quantized model is a vision and language understanding model that supports multi-domain applications such as text generation inference, image caption generation, and optical character recognition. It is built based on the transformers library.

MultimodalTransformersMultiple Languages