Best AI tools for< Text Processing >

NLTK (Natural Language Toolkit) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries, and an active discussion forum. Thanks to a hands-on guide introducing programming fundamentals alongside topics in computational linguistics, plus comprehensive API documentation, NLTK is suitable for linguists, engineers, students, educators, researchers, and industry users alike.

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.

1minAI is a free all-in-one AI application that offers various AI features for text, image, audio, and video processing. It provides tools like image generation, text removal, background replacement, and more. With no AI training required, the platform ensures user data privacy. Users can access top AI tools for tasks like content creation, design, social media management, and more. The application offers reasonable pricing plans with no hidden fees and secure payment options. Users can earn free credits through daily visits, reviews, and referrals.

GetDigest is an AI-powered tool that provides lightning-fast document summarization. It can analyze web content and text documents in over 33 languages, summarizing them efficiently by ignoring irrelevant information. The technology is designed to help users process information more effectively, saving time and enhancing productivity. GetDigest offers businesses the opportunity to integrate its technology into their own infrastructure or software projects, supporting various text formats, web environments, archives, emails, and image formats.

Atua is an AI tool designed to provide seamless access to ChatGPT on Mac devices. It allows users to easily interact with ChatGPT through custom commands and shortcut keys, enabling tasks such as text rephrasing, grammar correction, content expansion, and more. Atua offers effortless text selection and processing, conversation history saving, and limitless use cases for ChatGPT across various domains. The tool ensures user privacy by storing data locally and not monitoring or sending any analytics.

Floneum is a versatile AI-powered tool designed for language-related tasks. It allows users to build workflows using large language models through a user-friendly drag-and-drop interface. Additionally, Floneum supports the secure extension of functionalities with WebAssembly plugins, enabling users to write plugins in various languages like Rust, C, Java, or Go. With 41 built-in plugins, Floneum offers a range of features to enhance text processing, search engine operations, file handling, Python script execution, browser automation, and more.

Chad AI is an AI-powered chatbot application that leverages advanced neural networks like GPT-4o, Midjourney, Stable Diffusion, and Dall-E to provide users with quick and efficient responses in Russian. The application supports text and code processing, content creation, image generation, and text improvement. It offers a range of subscription plans to cater to different user needs and preferences, ensuring seamless access to cutting-edge AI technologies for various tasks.

Torq AI is an advanced productivity assistant powered by ChatGPT, designed to revolutionize productivity through AI assistance. It offers features such as efficient email communication, powerful text processing, integrated ChatGPT and Google searches, and data insights. Torq AI aims to make users 200x more productive by providing seamless and interactive AI-powered solutions.

Macaify is an AI application designed to bring AI capabilities to any Mac app with just a shortcut key. Users can unlock various AI smarts, customize predefined robots, and access over 1000 robot templates for text processing, code generation, and automation tasks. The application allows for mouse-free operation and offers features like generating images, searching images, converting text to speech files, bridging system and internet interfaces, processing web URLs, and searching the latest internet content. Macaify is free to use, with different pricing plans offering additional AI capabilities and support.

GPT-4O is a free all-in-one OpenAI tool that offers advanced AI capabilities for online solutions. It enhances productivity, creativity, and problem-solving by providing real-time text, vision, and audio processing. With features like instantaneous interaction, integrated multimodal processing, and advanced emotion detection, GPT-4O revolutionizes user experiences across various industries. Its broad accessibility democratizes access to cutting-edge AI technology, empowering users globally.

Rizemail is an AI-powered email summarization tool that helps users quickly get to the core of their unread newsletters, long email threads, and cluttered commercial communications. By forwarding an email to [email protected], the tool uses AI to summarize the content and returns the key information you need, all within your inbox. Rizemail aims to save users time by providing fast and secure email summarization services, with a focus on user privacy and convenience.

Medallia is an AI-powered text analytics software that enables users to uncover high-impact insights and drive actions with real-time, human-centric text analytics. It offers comprehensive feedback capture, role-based reporting, AI & analytics, integrations, and enterprise-grade security. The platform helps organizations analyze unstructured data, derive hidden meanings behind words, create customizable KPIs, and build out-of-the-box topic models for various industries and use cases.

ChatGPT4o is OpenAI's latest flagship model, capable of processing text, audio, image, and video inputs, and generating corresponding outputs. It offers both free and paid usage options, with enhanced performance in English and coding tasks, and significantly improved capabilities in processing non-English languages. ChatGPT4o includes built-in safety measures and has undergone extensive external testing to ensure safety. It supports multimodal inputs and outputs, with advantages in response speed, language support, and safety, making it suitable for various applications such as real-time translation, customer support, creative content generation, and interactive learning.

LLM Quality Beefer-Upper is an AI tool designed to enhance the quality and productivity of LLM responses by automating critique, reflection, and improvement. Users can generate multi-agent prompt drafts, choose from different quality levels, and upload knowledge text for processing. The application aims to maximize output quality by utilizing the best available LLM models in the market.

GPTKit is a free AI text generation detection tool that utilizes six different AI-based content detection techniques to identify and classify text as either human- or AI-generated. It provides reports on the authenticity and reality of the analyzed content, with an accuracy of approximately 93%. The first 2048 characters in every request are free, and users can register for free to get 2048 characters/request.

Base64.ai is an automated document processing API that offers a leading no-code AI solution for understanding documents, photos, and videos. It provides a comprehensive set of features for document processing across various industries, with a strong focus on accuracy, security, and extensibility. Base64.ai is designed to streamline document automation processes and improve data extraction efficiency.

Rytar is an AI-powered writing platform that helps users generate unique, relevant, and high-quality content in seconds. It uses state-of-the-art AI writing models to generate articles, blog posts, website pages, and other types of content from just a headline or a few keywords. Rytar is designed to help users save time and effort in the content creation process, and to produce content that is optimized for SEO and readability.

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.

ConversAI is an AI-powered chat assistant designed to enhance online communication. It uses natural language processing and machine learning to understand and respond to messages in a conversational manner. With ConversAI, users can quickly generate personalized responses, summarize long messages, detect the tone of conversations, communicate in multiple languages, and even add GIFs to their replies. It integrates seamlessly with various messaging platforms and tools, making it easy to use and efficient. ConversAI helps users save time, improve their communication skills, and have more engaging conversations online.

Datumbox is a machine learning platform that offers a powerful open-source Machine Learning Framework written in Java. It provides a large collection of algorithms, models, statistical tests, and tools to power up intelligent applications. The platform enables developers to build smart software and services quickly using its REST Machine Learning API. Datumbox API offers off-the-shelf Classifiers and Natural Language Processing services for applications like Sentiment Analysis, Topic Classification, Language Detection, and more. It simplifies the process of designing and training Machine Learning models, making it easy for developers to create innovative applications.

The `unstructured` library provides open-source components for ingesting and pre-processing images and text documents, such as PDFs, HTML, Word docs, and many more. The use cases of `unstructured` revolve around streamlining and optimizing the data processing workflow for LLMs. `unstructured` modular functions and connectors form a cohesive system that simplifies data ingestion and pre-processing, making it adaptable to different platforms and efficient in transforming unstructured data into structured outputs.

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.

**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.

AnyGPT is a unified multimodal language model that utilizes discrete representations for processing various modalities like speech, text, images, and music. It aligns the modalities for intermodal conversions and text processing. AnyInstruct dataset is constructed for generative models. The model proposes a generative training scheme using Next Token Prediction task for training on a Large Language Model (LLM). It aims to compress vast multimodal data on the internet into a single model for emerging capabilities. The tool supports tasks like text-to-image, image captioning, ASR, TTS, text-to-music, and music captioning.

PostgresML is a powerful Postgres extension that seamlessly combines data storage and machine learning inference within your database. It enables running machine learning and AI operations directly within PostgreSQL, leveraging GPU acceleration for faster computations, integrating state-of-the-art large language models, providing built-in functions for text processing, enabling efficient similarity search, offering diverse ML algorithms, ensuring high performance, scalability, and security, supporting a wide range of NLP tasks, and seamlessly integrating with existing PostgreSQL tools and client libraries.

Awesome Khmer Language is a comprehensive collection of resources for the Khmer language, including tools, datasets, research papers, projects/models, blogs/slides, and miscellaneous items. It covers a wide range of topics related to Khmer language processing, such as character normalization, word segmentation, part-of-speech tagging, optical character recognition, text-to-speech, and more. The repository aims to support the development of natural language processing applications for the Khmer language by providing a diverse set of resources and tools for researchers and developers.

LLM-Minutes-of-Meeting is a project showcasing NLP & LLM's capability to summarize long meetings and automate the task of delegating Minutes of Meeting(MoM) emails. It converts audio/video files to text, generates editable MoM, and aims to develop a real-time python web-application for meeting automation. The tool features keyword highlighting, topic tagging, export in various formats, user-friendly interface, and uses Celery for asynchronous processing. It is designed for corporate meetings, educational institutions, legal and medical fields, accessibility, and event coverage.

AgentScope is a multi-agent platform designed to empower developers to build multi-agent applications with large-scale models. It features three high-level capabilities: Easy-to-Use, High Robustness, and Actor-Based Distribution. AgentScope provides a list of `ModelWrapper` to support both local model services and third-party model APIs, including OpenAI API, DashScope API, Gemini API, and ollama. It also enables developers to rapidly deploy local model services using libraries such as ollama (CPU inference), Flask + Transformers, Flask + ModelScope, FastChat, and vllm. AgentScope supports various services, including Web Search, Data Query, Retrieval, Code Execution, File Operation, and Text Processing. Example applications include Conversation, Game, and Distribution. AgentScope is released under Apache License 2.0 and welcomes contributions.

AI Coding Assistant is a free and open-source IntelliJ plugin that leverages cutting-edge Language Model APIs to enhance developers' coding experience. It seamlessly integrates with various leading LLM APIs, offers an intuitive toolbar UI, and allows granular control over API requests. With features like Code & Patch Chat, Planning with AI Agents, Markdown visualization, and versatile text processing capabilities, this tool aims to streamline coding workflows and boost productivity.

BasicLingua is a Python library that provides functionalities for linguistic tasks such as tokenization, stemming, lemmatization, and many others. It is based on the Gemini Language Model, which has demonstrated promising results in dealing with text data. BasicLingua can be used as an API or through a web demo. It is available under the MIT license and can be used in various projects.

Unitxt is a customizable library for textual data preparation and evaluation tailored to generative language models. It natively integrates with common libraries like HuggingFace and LM-eval-harness and deconstructs processing flows into modular components, enabling easy customization and sharing between practitioners. These components encompass model-specific formats, task prompts, and many other comprehensive dataset processing definitions. The Unitxt-Catalog centralizes these components, fostering collaboration and exploration in modern textual data workflows. Beyond being a tool, Unitxt is a community-driven platform, empowering users to build, share, and advance their pipelines collaboratively.

PaperQA is a minimal package for question and answering from PDFs or text files, providing very good answers with in-text citations. It uses OpenAI Embeddings to embed and search documents, and includes a process of embedding docs, queries, searching for top passages, creating summaries, using an LLM to re-score and select relevant summaries, putting summaries into prompt, and generating answers. The tool can be used to answer specific questions related to scientific research by leveraging citations and relevant passages from documents.

TTS Generation WebUI is a comprehensive tool that provides a user-friendly interface for text-to-speech and voice cloning tasks. It integrates various AI models such as Bark, MusicGen, AudioGen, Tortoise, RVC, Vocos, Demucs, SeamlessM4T, and MAGNeT. The tool offers one-click installers, Google Colab demo, videos for guidance, and extra voices for Bark. Users can generate audio outputs, manage models, caches, and system space for AI projects. The project is open-source and emphasizes ethical and responsible use of AI technology.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

Woodpecker is a tool designed to correct hallucinations in Multimodal Large Language Models (MLLMs) by introducing a training-free method that picks out and corrects inconsistencies between generated text and image content. It consists of five stages: key concept extraction, question formulation, visual knowledge validation, visual claim generation, and hallucination correction. Woodpecker can be easily integrated with different MLLMs and provides interpretable results by accessing intermediate outputs of the stages. The tool has shown significant improvements in accuracy over baseline models like MiniGPT-4 and mPLUG-Owl.

The GenAI repository contains pipelines that implement image and text generation tasks. The implementation uses OpenVINO capabilities to optimize the pipelines. Each sample covers a family of models and suggests certain modifications to adapt the code to specific needs. It includes the following pipelines: 1. Benchmarking script for large language models 2. Text generation C++ samples that support most popular models like LLaMA 2 3. Stable Diffuison (with LoRA) C++ image generation pipeline 4. Latent Consistency Model (with LoRA) C++ image generation pipeline

NeMo Curator is a GPU-accelerated open-source framework designed for efficient large language model data curation. It provides scalable dataset preparation for tasks like foundation model pretraining, domain-adaptive pretraining, supervised fine-tuning, and parameter-efficient fine-tuning. The library leverages GPUs with Dask and RAPIDS to accelerate data curation, offering customizable and modular interfaces for pipeline expansion and model convergence. Key features include data download, text extraction, quality filtering, deduplication, downstream-task decontamination, distributed data classification, and PII redaction. NeMo Curator is suitable for curating high-quality datasets for large language model training.

VectorETL is a lightweight ETL framework designed to assist Data & AI engineers in processing data for AI applications quickly. It streamlines the conversion of diverse data sources into vector embeddings and storage in various vector databases. The framework supports multiple data sources, embedding models, and vector database targets, simplifying the creation and management of vector search systems for semantic search, recommendation systems, and other vector-based operations.

Pandrator is a GUI tool for generating audiobooks and dubbing using voice cloning and AI. It transforms text, PDF, EPUB, and SRT files into spoken audio in multiple languages. It leverages XTTS, Silero, and VoiceCraft models for text-to-speech conversion and voice cloning, with additional features like LLM-based text preprocessing and NISQA for audio quality evaluation. The tool aims to be user-friendly with a one-click installer and a graphical interface.

Awesome AI is a curated list of artificial intelligence resources including courses, tools, apps, and open-source projects. It covers a wide range of topics such as machine learning, deep learning, natural language processing, robotics, conversational interfaces, data science, and more. The repository serves as a comprehensive guide for individuals interested in exploring the field of artificial intelligence and its applications across various domains.

Traitement texte, image, vidéo, génération image

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

input text and outputs scrambled readable text

With this bot process of making notes will be easier. Send your text and wait for the result

Generate and explain regex patterns from your description, it support English and Chinese.

What is Alien lyrics meaning? Alien singer：P. Sears, J. Sears，album：Modern Times ，album_time：1981. Click The LINK For More ↓↓↓

A specialist in crafting and explaining regex patterns.

Expert in crafting and explaining grep commands.

Efficient image processing with text overlays and memory reset.

Full step by step guidance and copy & paste text for developing assistants with specific use cases.

Translates CN to EN for processing, outputs in CN.

Transforms complex texts into simple, understandable language.

Open Domain Relation Extractor

An editor that refines and enhances your writing.

Compares and reports differences in text files

Visual and friendly guide for image processing.

Converts graph triples to clear text, no assumptions.

A text and content creation AI.

📖 Political speeches for patterns and biases.

Assists with text extraction from PDFs