The Best 897 Image-to-Text Tools in 2025

CLIP is a vision-language model developed by OpenAI that maps images and text into a shared embedding space through contrastive learning, supporting zero-shot image classification.

CLIP is a multimodal model developed by OpenAI that can understand the relationship between images and text, supporting zero-shot image classification tasks.

SigLIP is a vision-language model pre-trained on the WebLi dataset, employing an improved sigmoid loss function to optimize image-text matching tasks.

CLIP is a multimodal model developed by OpenAI that maps images and text into a shared embedding space through contrastive learning, enabling zero-shot image classification capabilities.

BLIP is an advanced vision-language pretrained model, excelling in image captioning tasks and supporting both conditional and unconditional text generation.

BLIP is a unified vision-language pretraining framework, excelling at image caption generation tasks, supporting both conditional and unconditional image caption generation.

OpenVLA 7B is an open-source vision-language-action model trained on the Open X-Embodiment dataset, capable of generating robot actions based on language instructions and camera images.

LLaVA is an open-source multimodal chatbot, fine-tuned based on LLaMA/Vicuna, supporting image-text interaction.

This is an image captioning model based on ViT and GPT2 architectures, capable of generating natural language descriptions for input images.

BLIP-2 is a vision-language model that combines an image encoder with a large language model for image-to-text generation tasks.

SigLIP 2 is a vision-language model based on the SigLIP pre-training objective, integrating multiple technologies to enhance semantic understanding, localization, and dense feature extraction capabilities.

Gemma is a lightweight, advanced open model series launched by Google, built on the same research and technology as Gemini. Gemma 3 is a multimodal model capable of processing both text and image inputs to generate text outputs.

A LLaVA model fine-tuned based on Meta-Llama-3-8B-Instruct and CLIP-ViT-Large-patch14-336, supporting image-text-to-text tasks

Phi-3.5-vision is a lightweight, cutting-edge open multimodal model supporting 128K context length, focusing on high-quality, reasoning-rich text and visual data.

Gemma is a lightweight cutting-edge open model series launched by Google, built on the same technology as Gemini, supporting multimodal input and text output.

GIT is a dual-conditional Transformer decoder based on CLIP image tokens and text tokens, designed for image-to-text generation tasks.

Gemma is a lightweight cutting-edge open-source multimodal model series launched by Google, built on the technology used to create Gemini models, supporting text and image inputs to generate text outputs.

SigLIP is a vision-language model pretrained on the WebLi dataset, utilizing an improved Sigmoid loss function to optimize image-text matching tasks

SigLIP is a multimodal model pretrained on the WebLi dataset, utilizing an improved Sigmoid loss function, suitable for zero-shot image classification and image-text retrieval tasks.

BLIP-2 is a vision-language model that combines an image encoder with a large language model for image-to-text generation and visual question answering tasks.

TrOCR is a Transformer-based optical character recognition model specifically designed for handwritten text recognition.

Moondream is a lightweight vision-language model designed for efficient operation across all platforms.

Kosmos-2 is a multimodal large language model capable of understanding and generating text descriptions related to images, and establishing associations between text and image regions.

Donut is an OCR-free document understanding Transformer model, fine-tuned on the DocVQA dataset, capable of directly extracting and comprehending text information from images.

BiomedCLIP is a biomedical vision-language foundation model pre-trained via contrastive learning on the PMC-15M dataset, supporting cross-modal retrieval, image classification, visual question answering, and other tasks.

Donut is an OCR-free document understanding Transformer model that combines a visual encoder and text decoder to process document images.

MiniCPM-V 2.6 is a multimodal vision-language model supporting image-to-text conversion with multilingual processing capabilities.

BLIP-2 is a vision-language model based on Flan T5-xl, pre-trained by freezing the image encoder and large language model, supporting tasks such as image captioning and visual question answering.

MiniCPM-V is a mobile GPT-4V-level multimodal large language model that supports single-image, multi-image, and video understanding, equipped with visual and optical character recognition capabilities.

H2OVL-Mississippi-2B is a high-performance general-purpose vision-language model developed by H2O.ai, capable of handling a wide range of multimodal tasks. This model has 2 billion parameters and performs excellently in tasks such as image captioning, visual question answering (VQA), and document understanding.

A vision-language generation model fine-tuned based on google/flan-t5-xxl, specifically designed for image-text retrieval tasks

Florence-2-SD3-Captioner is an image caption generation model based on the Florence-2 architecture, specifically designed for generating high-quality image captions.

An 800M-parameter vision-language model from H2O.ai, specializing in OCR and document understanding with excellent performance

A 1.6B-parameter multimodal model combining SigLIP and Phi-1.5 architectures, supporting image understanding and Q&A tasks

Gemma is a lightweight open-source multimodal model series launched by Google. It supports text and image inputs and generates text outputs. It has a 128K large context window and supports over 140 languages.

SmolVLM2-2.2B is a lightweight multimodal model designed for analyzing video content. It can process video, image, and text inputs and generate text outputs.

This is an image-to-text model based on the MIT license, capable of converting image content into descriptive text.

Florence-2 is an advanced vision foundation model developed by Microsoft, employing a prompt-based approach to handle a wide range of vision and vision-language tasks.

Gemma is a series of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create Gemini models.

Gemma is a lightweight open-source multimodal model series launched by Google, built on the same technology as Gemini, supporting text and image inputs and generating text outputs.

This is a medical imaging report generation model based on the VisionEncoderDecoder architecture, specifically designed to generate radiology report texts from chest X-ray images.

This is a text generation model based on chest X-ray images, capable of generating radiology impression reports from medical imaging.