AI tools for convertimage to prompt
Related Tools:
Fronty
Fronty is an AI-powered tool that converts images to HTML CSS code, allowing users to create websites quickly and easily. It offers features such as AI-powered image to HTML CSS conversion, no-code website editing, and website hosting services. Fronty has been used by over 300k users to create over 1 million websites, with a focus on providing high-quality results and user satisfaction.
Picture to Text Converter
Picture to Text Converter is an online tool that uses Optical Character Recognition (OCR) technology to extract text from images. It can process various image formats like JPG, PNG, GIF, scanned documents (PDFs), and even photos taken with your phone's camera. The extracted text can be copied to the clipboard or downloaded as a TXT file. Picture to Text Converter is free to use and does not require any registration or installation. It is a convenient and efficient way to convert images into editable text.
Waifu2x
Waifu2x is a website that offers Single-Image Super-Resolution for Anime-Style Art using Deep Convolutional Neural Networks. Users can enhance the quality of their images by upscaling and reducing noise. The site supports various languages and provides detailed instructions on image processing. It also offers options for noise reduction and upscaling, with limits on file size and dimensions. Additionally, users can choose different styles for their images and save them in different formats like PNG and WebP.
Jenson Type Designer
Design your own fonts from text or image inspiration with this adaptive typography mastermind. Share a text description or image and get a proof of concept, full font character sheet, and marketing promo image for the new typeface, step by step.
Favicon Wizard
Upload your brand logo or other favorite brand image asset and we'll create a favicon for you!
text-extract-api
The text-extract-api is a powerful tool that allows users to convert images, PDFs, or Office documents to Markdown text or JSON structured documents with high accuracy. It is built using FastAPI and utilizes Celery for asynchronous task processing, with Redis for caching OCR results. The tool provides features such as PDF/Office to Markdown and JSON conversion, improving OCR results with LLama, removing Personally Identifiable Information from documents, distributed queue processing, caching using Redis, switchable storage strategies, and a CLI tool for task management. Users can run the tool locally or on cloud services, with support for GPU processing. The tool also offers an online demo for testing purposes.
Old-Persian-Cuneiform-OCR
This repository aims to create an OCR model for Old Persian Cuneiform. It includes three OCR models: yolo_cnn_old_persian, tesseract_old_persian, and easyocr_old_persian. The status of these models varies from incomplete to completed but needing optimization. Users can train and use the models for converting Old Persian Cuneiform images to text. The repository also provides resources such as trainer notebooks and pre-trained models for easy access and implementation.
vectordb-recipes
This repository contains examples, applications, starter code, & tutorials to help you kickstart your GenAI projects. * These are built using LanceDB, a free, open-source, serverless vectorDB that **requires no setup**. * It **integrates into python data ecosystem** so you can simply start using these in your existing data pipelines in pandas, arrow, pydantic etc. * LanceDB has **native Typescript SDK** using which you can **run vector search** in serverless functions! This repository is divided into 3 sections: - Examples - Get right into the code with minimal introduction, aimed at getting you from an idea to PoC within minutes! - Applications - Ready to use Python and web apps using applied LLMs, VectorDB and GenAI tools - Tutorials - A curated list of tutorials, blogs, Colabs and courses to get you started with GenAI in greater depth.
e2m
E2M is a Python library that can parse and convert various file types into Markdown format. It supports the conversion of multiple file formats, including doc, docx, epub, html, htm, url, pdf, ppt, pptx, mp3, and m4a. The ultimate goal of the E2M project is to provide high-quality data for Retrieval-Augmented Generation (RAG) and model training or fine-tuning. The core architecture consists of a Parser responsible for parsing various file types into text or image data, and a Converter responsible for converting text or image data into Markdown format.