Best AI tools for< Extract Text From Pdf >

FileDrop is a file or document manager that allows you to drag and drop files into a document with automatic linking and save them to Google Drive. It also offers features like OCR, translation, and AI integration. With FileDrop, you can easily insert, save, and link files in Google Sheets cells, Docs, and Slides.

Swiftask is an all-in-one AI Assistant designed to enhance individual and team productivity and creativity. It integrates a range of AI technologies, chatbots, and productivity tools into a cohesive chat interface. Swiftask offers features such as generating text, language translation, creative content writing, answering questions, extracting text from images and PDFs, table and form extraction, audio transcription, speech-to-text conversion, AI-based image generation, and project management capabilities. Users can benefit from Swiftask's comprehensive AI solutions to work smarter and achieve more.

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.

Extractify.co is a website that offers a range of text extraction services. It allows users to extract text from various sources such as images, PDFs, and websites. The platform uses advanced algorithms to accurately extract and convert text into editable formats. Users can easily copy, edit, and save the extracted text for their needs. Extractify.co provides a user-friendly interface and supports multiple languages, making it a versatile tool for text extraction tasks.

GetSearchablePDF is an online tool that allows users to convert scanned or image-based PDF documents into searchable PDFs. With its advanced OCR (Optical Character Recognition) technology, the tool accurately extracts text from images, making the resulting PDFs easy to search, edit, and share. The process is simple and straightforward: users simply connect their Dropbox or OneDrive account, drag and drop their PDF files into the designated folder, and the tool automatically converts them into searchable PDFs.

GrabText is an online OCR tool that allows users to convert handwritten or printed text from photos, graphics, or documents into editable text. It uses ChatGPT to automatically correct spelling, grammar, and other illegal writings. The tool also supports math equations and offers flexible output options such as txt, latex, doc, and pdf.

Docubase.ai is a powerful document analysis tool that uses advanced natural language processing and machine learning to extract information and provide relevant answers to your queries. It can automatically extract text content from uploaded documents, generate relevant questions, and extract answers from the document content. Docubase.ai supports a wide range of document formats, including PDF, Word, Excel, PowerPoint, and text documents. It also allows users to ask their own questions and provides options to export answers in different formats for easy sharing and documentation.

Honeybear.ai is an AI tool designed to simplify document reading tasks. It utilizes advanced algorithms to extract and analyze text from various documents, making it easier for users to access and comprehend information. With Honeybear.ai, users can streamline their document processing workflows and enhance productivity.

Chat PDF AI Online is an advanced AI tool that revolutionizes the way users interact with PDF documents. It offers cutting-edge AI features to enhance the PDF experience, providing seamless solutions for reading, summarizing, analyzing, and translating PDF files. With features like longer context support, powerful tabular data analysis, and advanced LLM support, Chat PDF AI Online ensures smarter and faster document processing. Users can securely upload and process large PDF files, benefiting from high accuracy and efficiency in document handling.

ChatPDF is an AI-powered tool that allows users to interact with PDF documents in a conversational manner. It uses natural language processing (NLP) to understand user queries and provide relevant information or perform actions on the PDF. With ChatPDF, users can ask questions about the content of the PDF, search for specific information, extract data, translate text, and more, all through a simple chat-like interface.

Shortify is a tool that helps you summarize text from various sources, including articles, YouTube videos, PDFs, and more. It integrates with your existing apps, allowing you to easily summarize content by tapping the Share button and selecting Shortify. The summarized text is presented in a concise and easy-to-read format, saving you time and effort. Shortify also offers additional features such as ultra-short summaries, sharing options, and usage statistics.

basebox is an AI application designed to provide secure and efficient AI solutions for businesses across various industries. It offers a range of features such as secure text editing, data extraction from PDFs and Excel documents, academic text summarization, multilingual translation, and blog post creation. With a focus on data privacy and security, basebox ensures end-to-end encryption, GDPR compliance, and hosting in Europe. The application is user-friendly, requiring no technical expertise for setup, and offers transparent pricing based on actual usage.

PrivacyDoc is an AI-powered portal that allows users to analyze and query PDF and ebooks effortlessly. By leveraging advanced NLP technology, PrivacyDoc enables users to uncover insights and conduct thorough document analysis. The platform offers features such as easy file upload, query functionality, enhanced security measures, and free access to powerful PDF analysis tools. With PrivacyDoc, users can experience the convenience of logging in with their Google account, submitting queries for prompt AI-driven responses, and ensuring data privacy with secure file handling.

LedgerBox is an AI tool that specializes in converting bank statements into digital formats. It simplifies the process of managing financial data by automatically extracting and organizing information from bank statements. With LedgerBox, users can easily convert paper-based bank statements into digital files, enabling quick and efficient financial analysis and reporting. The tool is designed to save time and reduce errors associated with manual data entry, making it a valuable asset for individuals and businesses looking to streamline their financial processes.

The website offers an AI-powered PDF reader that allows users to chat with any PDF document. Users can upload a PDF, ask questions, get answers, extract precise sections of text, summarize, annotate, highlight, classify, analyze, translate, and more. The AI tool helps in quickly identifying key details, finding answers without reading through every word, and citing sources. It is ideal for professionals in various fields like legal, finance, research, academia, healthcare, and public sector, as well as students. The tool aims to save time, increase productivity, and simplify document management and analysis.

iTextMaster is an AI-powered tool that allows users to analyze, summarize, and chat with text-based documents, including PDFs and web pages. It utilizes ChatGPT technology to provide intelligent answers to questions and extract key information from documents. The tool is designed to simplify text processing, improve understanding efficiency, and save time. iTextMaster supports multiple languages and offers a user-friendly interface for easy navigation and interaction.

Summarize This is an AI-powered tool that provides instant summaries for text, PDFs, websites, and YouTube videos. It leverages the power of AI to transform content into concise summaries, making information gathering quick and effortless. Users can capture the essence of any text, extract main points from web pages, skip to important parts of videos, and save time by summarizing PDFs. The tool also offers the ability to summarize content on iPhones and Chrome browsers, providing a streamlined summarization experience across various platforms.

Skimming is an AI tool that enables users to interact with various types of data, including audio, video, and text, to extract knowledge. It offers features like chatting with documents, YouTube videos, websites, audio, and video, as well as custom prompts and multilingual support. Skimming is trusted by over 100,000 users and is designed to save time and enhance information extraction. The tool caters to a diverse audience, including teachers, students, businesses, researchers, scholars, lawyers, HR professionals, YouTubers, and podcasters.

GenIQ is an AI-powered application that allows users to interact with files through natural language. It generates concise summaries of lengthy documents using Generative AI technology. Users can work with various file types such as audio, video, PDF, and Word documents by asking questions in natural language and receiving real-time answers. GenIQ offers features like audio & video analysis, document processing, multilingual support, and handwritten document analysis.

ChatInDoc is an AI-powered tool designed to revolutionize the way people interact with and comprehend lengthy documents. By leveraging cutting-edge AI technology, ChatInDoc offers users the ability to efficiently analyze, summarize, and extract key information from various file formats such as PDFs, Office documents, and text files. With features like IR analysis, term lookup, PDF viewing, and AI-powered chat capabilities, ChatInDoc aims to streamline the process of digesting complex information and enhance productivity. The application's user-friendly interface and advanced AI algorithms make it a valuable tool for students, professionals, and anyone dealing with extensive document reading tasks.

ChatWeb is a tool that can crawl web pages, extract text from PDF, DOCX, TXT files, and generate an embedded summary. It can answer questions based on text content using chatAPI and embeddingAPI based on GPT3.5. The tool calculates similarity scores between text vectors to generate summaries, performs nearest neighbor searches, and designs prompts to answer user questions. It aims to extract relevant content from text and provide accurate search results based on keywords. ChatWeb supports various modes, languages, and settings, including temperature control and PostgreSQL integration.

Extractor is an AI-powered data extraction library for Laravel that leverages OpenAI's capabilities to effortlessly extract structured data from various sources, including images, PDFs, and emails. It features a convenient wrapper around OpenAI Chat and Completion endpoints, supports multiple input formats, includes a flexible Field Extractor for arbitrary data extraction, and integrates with Textract for OCR functionality. Extractor utilizes JSON Mode from the latest GPT-3.5 and GPT-4 models, providing accurate and efficient data extraction.

Swift OCR is a powerful tool for extracting text from PDF files using OpenAI's GPT-4 Turbo with Vision model. It offers flexible input options, advanced OCR processing, performance optimizations, structured output, robust error handling, and scalable architecture. The tool ensures accurate text extraction, resilience against failures, and efficient handling of multiple requests.

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

LLMWare is a framework for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows. This project provides a comprehensive set of tools that anyone can use - from a beginner to the most sophisticated AI developer - to rapidly build industrial-grade, knowledge-based enterprise LLM applications. Our specific focus is on making it easy to integrate open source small specialized models and connecting enterprise knowledge safely and securely.

HuggingFists is a low-code data flow tool that enables convenient use of LLM and HuggingFace models. It provides functionalities similar to Langchain, allowing users to design, debug, and manage data processing workflows, create and schedule workflow jobs, manage resources environment, and handle various data artifact resources. The tool also offers account management for users, allowing centralized management of data source accounts and API accounts. Users can access Hugging Face models through the Inference API or locally deployed models, as well as datasets on Hugging Face. HuggingFists supports breakpoint debugging, branch selection, function calls, workflow variables, and more to assist users in developing complex data processing workflows.

This solution showcases how to summarize a large corpus of documents using Generative AI. It provides an end-to-end demonstration of document summarization going all the way from raw documents, detecting text in the documents and summarizing the documents on-demand using Vertex AI LLM APIs, Cloud Vision Optical Character Recognition (OCR) and BigQuery.

Gemini PDF Chatbot is a Streamlit-based application that allows users to chat with a conversational AI model trained on PDF documents. The chatbot extracts information from uploaded PDF files and answers user questions based on the provided context. It features PDF upload, text extraction, conversational AI using the Gemini model, and a chat interface. Users can deploy the application locally or to the cloud, and the project structure includes main application script, environment variable file, requirements, and documentation. Dependencies include PyPDF2, langchain, Streamlit, google.generativeai, and dotenv.

LARS is an application that enables users to run Large Language Models (LLMs) locally on their devices, upload their own documents, and engage in conversations where the LLM grounds its responses with the uploaded content. The application focuses on Retrieval Augmented Generation (RAG) to increase accuracy and reduce AI-generated inaccuracies. LARS provides advanced citations, supports various file formats, allows follow-up questions, provides full chat history, and offers customization options for LLM settings. Users can force enable or disable RAG, change system prompts, and tweak advanced LLM settings. The application also supports GPU-accelerated inferencing, multiple embedding models, and text extraction methods. LARS is open-source and aims to be the ultimate RAG-centric LLM application.

The Pipe is a multimodal-first tool for feeding files and web pages into vision-language models such as GPT-4V. It is best for LLM and RAG applications that require a deep understanding of tricky data sources. The Pipe is available as a hosted API at thepi.pe, or it can be set up locally.

Extractous offers a fast and efficient solution for extracting content and metadata from various document types such as PDF, Word, HTML, and many other formats. It is built with Rust, providing high performance, memory safety, and multi-threading capabilities. The tool eliminates the need for external services or APIs, making data processing pipelines faster and more efficient. It supports multiple file formats, including Microsoft Office, OpenOffice, PDF, spreadsheets, web documents, e-books, text files, images, and email formats. Extractous provides a clear and simple API for extracting text and metadata content, with upcoming support for JavaScript/TypeScript. It is free for commercial use under the Apache 2.0 License.

A Telegram bot for accessing Google Gemini, MS Bing, etc. The bot responds to the keywords 'bot' and 'google' to provide information. It can handle voice messages, text files, images, and links. It can generate images based on descriptions, extract text from images, and summarize content. The bot can interact with various AI models and perform tasks like voice control, text-to-speech, and text recognition. It supports long texts, large responses, and file transfers. Users can interact with the bot using voice commands and text. The bot can be customized for different AI providers and has features for both users and administrators.

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.

The LLM-Aided OCR Project is an advanced system that enhances Optical Character Recognition (OCR) output by leveraging natural language processing techniques and large language models. It offers features like PDF to image conversion, OCR using Tesseract, error correction using LLMs, smart text chunking, markdown formatting, duplicate content removal, quality assessment, support for local and cloud-based LLMs, asynchronous processing, detailed logging, and GPU acceleration. The project provides detailed technical overview, text processing pipeline, LLM integration, token management, quality assessment, logging, configuration, and customization. It requires Python 3.12+, Tesseract OCR engine, PDF2Image library, PyTesseract, and optional OpenAI or Anthropic API support for cloud-based LLMs. The installation process involves setting up the project, installing dependencies, and configuring environment variables. Users can place a PDF file in the project directory, update input file path, and run the script to generate post-processed text. The project optimizes processing with concurrent processing, context preservation, and adaptive token management. Configuration settings include choosing between local or API-based LLMs, selecting API provider, specifying models, and setting context size for local LLMs. Output files include raw OCR output and LLM-corrected text. Limitations include performance dependency on LLM quality and time-consuming processing for large documents.

WARC-GPT is an experimental retrieval augmented generation pipeline for web archive collections. It allows users to interact with WARC files, extract text, generate text embeddings, visualize embeddings, and interact with a web UI and API. The tool is highly customizable, supporting various LLMs, providers, and embedding models. Users can configure the application using environment variables, ingest WARC files, start the server, and interact with the web UI and API to search for content and generate text completions. WARC-GPT is designed for exploration and experimentation in exploring web archives using AI.

The Second Brain AI Agent Project is a tool designed to empower personal knowledge management by automatically indexing markdown files and links, providing a smart search engine powered by OpenAI, integrating seamlessly with different note-taking methods, and enhancing productivity by accessing information efficiently. The system is built on LangChain framework and ChromaDB vector store, utilizing a pipeline to process markdown files and extract text and links for indexing. It employs a Retrieval-augmented generation (RAG) process to provide context for asking questions to the large language model. The tool is beneficial for professionals, students, researchers, and creatives looking to streamline workflows, improve study sessions, delve deep into research, and organize thoughts and ideas effortlessly.

GenAIScript is a scripting environment designed to facilitate file ingestion, prompt development, and structured data extraction. Users can define metadata and model configurations, specify data sources, and define tasks to extract specific information. The tool provides a convenient way to analyze files and extract desired content in a structured format. It offers a user-friendly interface for working with data and automating data extraction processes, making it suitable for various data processing tasks.

Kernel Memory (KM) is a multi-modal AI Service specialized in the efficient indexing of datasets through custom continuous data hybrid pipelines, with support for Retrieval Augmented Generation (RAG), synthetic memory, prompt engineering, and custom semantic memory processing. KM is available as a Web Service, as a Docker container, a Plugin for ChatGPT/Copilot/Semantic Kernel, and as a .NET library for embedded applications. Utilizing advanced embeddings and LLMs, the system enables Natural Language querying for obtaining answers from the indexed data, complete with citations and links to the original sources. Designed for seamless integration as a Plugin with Semantic Kernel, Microsoft Copilot and ChatGPT, Kernel Memory enhances data-driven features in applications built for most popular AI platforms.

UFO is a UI-focused dual-agent framework to fulfill user requests on Windows OS by seamlessly navigating and operating within individual or spanning multiple applications.

Ragtacts is a Clojure library that allows users to easily interact with Large Language Models (LLMs) such as OpenAI's GPT-4. Users can ask questions to LLMs, create question templates, call Clojure functions in natural language, and utilize vector databases for more accurate answers. Ragtacts also supports RAG (Retrieval-Augmented Generation) method for enhancing LLM output by incorporating external data. Users can use Ragtacts as a CLI tool, API server, or through a RAG Playground for interactive querying.

Assists with text extraction from PDFs

Assists with PDFs locally.

I create structured filenames for PDFs from OCR text.

Japanese OCR and text reader　日本語OCR　読み取り失敗文字の修復機能つき。テスト中

OCR specialist, transcribes images to text within markdown blocks.

I extract data and tables from PDFs to CSV, focusing on data privacy and precision.

Advanced web-to-PDF text analysis tool.

Extract insights seamlessly from diverse documents.

Expert in extracting and transcribing text from images.

ce GPT va recevoir des images dans lesquelles il y a des questions QCM codingame ou Problem Solving sur les sujets : Java, Hibernate, Angular, Spring Boot, SQL. Il doit extraire le texte depuis l'image et répondre au question QCM le plus rapidement possible.

Formatted text from screenshot

Converts tweet screenshots to text

写真から情報をJSONに変換

Expert in full-scale app development, coding, and OCR.

Open Domain Relation Extractor

Magically turning text from emails, lists and website content into spreadsheet tables

Generate a Structured description in typescript format from the image and generate an image from that description.　and OCR

I provide detailed summaries, critiques, and inferences on articles, papers, transcripts, websites, and more. Just give me text, a URL, or file to digest.

Takes in any text and extracts the wisdom from it like you spent 3 hours taking handwritten notes.

Nyimpulkeun sareng nimba poin konci tina téks, artikel, video, dokumén sareng seueur deui