Open-source alternatives to Ocrs

llamafile is a model bundler and local runtime that packages large language models and their execution logic into single, portable executable files. It provides a distribution format for zero-installation local execution, allowing users to run models on various operating systems without managing external library dependencies or environment configurations. The project differentiates itself by bundling model weights and the runtime into one self-extracting binary. This approach simplifies the distribution of AI models, as the combined file contains everything necessary to run the model immediat

Tesseract is a neural network-based optical character recognition engine designed to convert scanned images and digital documents into machine-readable, searchable text. It functions as both a command-line utility for automating large-scale digitization workflows and a cross-platform library that can be embedded into desktop, mobile, or server-side applications. By utilizing long short-term memory networks, the engine provides robust text extraction across more than one hundred languages and dozens of scripts. The project distinguishes itself through a sophisticated document layout analysis f

Tesseract-OCR-iOS is a native integration of the Tesseract engine for iOS applications. It provides on-device image recognition to identify and extract printed text from images, converting them into editable strings. The project enables local device execution, meaning image processing and text extraction occur entirely on the hardware without the use of external cloud services or network requests. It utilizes a C++ wrapper and an Objective-C bridge to interface high-level iOS APIs with the underlying Tesseract engine and Leptonica image processing library. The library supports document digit

Paperless is a self-hosted document management system designed to digitize, index, and archive paper documents. It functions as an optical character recognition system that converts scanned images and PDFs into a searchable digital library, providing a web-based interface for querying and retrieving documents from a database. The system features an automated file ingestion pipeline that monitors specific directories and email inboxes to process and import documents without manual uploading. To maintain a private archive, it includes on-disk encryption for sensitive files and the ability to or

PandaOCR is a desktop application for extracting text from images and screen captures using optical character recognition. It functions as a mathematical formula digitizer, a table data extractor, a multilingual translation utility, and a text-to-speech interface. The project distinguishes itself through specialized recognition routing that distributes data across different providers based on whether the content is standard text, tables, or formulas. It provides real-time software interface localization by rendering translated text layers directly over active application windows using coordin

RapidOCR is an offline deep-learning OCR engine that detects and recognizes text in images using ONNX Runtime, operating entirely without an internet connection. It provides a unified inference pipeline that runs across multiple platforms including Windows, Linux, macOS, Android, and Raspberry Pi, with programming language bindings for Python, C++, Java, and C#. The engine separates text detection and recognition into independent modules that can be swapped or fine-tuned individually, and abstracts the inference backend behind a unified interface allowing seamless switching between ONNX Runti

Tess-two is an optical character recognition tool and Android application designed to extract written text from images using the Tesseract engine. It functions as an image analysis utility for detecting visual artifacts, blur, and optical flow within local image files on Android devices. The project includes an image pre-processing suite used to clean and manipulate images to increase the accuracy of text recognition. This involves a pipeline that applies grayscale conversion and binarization before the recognition process. The software integrates native image processing and character analys

Docling is a multimodal content converter and document parser designed to transform PDFs, Office files, and HTML into structured Markdown or JSON for generative AI applications. It functions as an OCR document processor and a PDF layout analyzer that extracts tables, charts, and hierarchical structures while preserving the original page layout. The system operates as a local-first inference engine, allowing for the processing of sensitive data in air-gapped environments without external network connectivity. It can also be deployed as an API or a Model Context Protocol server to provide parsi

Sparrow is an LLM document extraction platform and vision-based inference engine designed to convert images and PDFs into validated structured data. It functions as an agentic workflow orchestrator that chains classification, extraction, and validation tasks into multi-step pipelines. The system distinguishes itself through a backend-agnostic inference layer that manages models across local GPUs, Apple Silicon, and cloud providers. It employs coordinate-based visual grounding to map extracted text to precise bounding box coordinates and utilizes hint-based model steering to guide attention an

Deep-Learning-Experiments is an educational resource providing a collection of structured notes and hands-on coding experiments focused on neural network theory and model development. The repository serves as a practical guide for building and optimizing machine learning architectures, ranging from basic perceptrons to modern generative models. The project utilizes interactive notebooks to combine live code with narrative text, allowing users to explore the mathematical principles and architectural concepts behind deep learning. It provides instructional materials that cover the end-to-end ma

This is an educational curriculum for building and training neural networks using PyTorch. It serves as a deep learning training guide and resource, providing a structured series of lessons on tensor computation and architecture development. The course uses an interactive learning model that synchronizes academic theory with practice. It pairs theoretical lecture slides with exercise-driven notebooks, requiring students to implement model logic within predefined templates to validate their conceptual understanding. The curriculum covers a broad range of deep learning capabilities, including

This project is a collection of TensorFlow 2.x machine learning tutorials and practical code examples. It serves as a deep learning implementation guide for constructing diverse neural network architectures, including convolutional, recurrent, and generative networks. The repository provides templates and examples for several specialized domains, including computer vision for image classification and object detection, natural language processing for text generation and language understanding, and generative AI for synthesizing data using adversarial networks and autoencoders. It also includes

This project is a deep learning curriculum and a collection of PyTorch tutorials designed for deep learning education. It provides a structured set of technical documents and runnable notebooks that translate theoretical machine learning concepts into executable code. The repository includes implementation guides for various neural network architectures, specifically covering convolutional, recurrent, and transformer-based models. It provides practical examples for building computer vision pipelines for object detection and semantic segmentation, as well as natural language processing tools f

Pdfcraft is a containerized service for self-managed PDF processing, editing, and conversion. It provides a toolkit for document manipulation, a multi-format converter, and OCR software to transform scanned documents into searchable and editable text. The project features a visual, node-based workflow editor that allows users to build automated pipelines by chaining together various PDF conversion and optimization operations. The service covers a broad range of capabilities, including document management for merging and splitting files, format conversion between PDFs and office documents or

This project is a scientific computing framework for the .NET ecosystem, providing a comprehensive suite of libraries for numerical analysis, statistics, and mathematical optimization. It serves as a foundational toolkit for developing applications in machine learning, digital signal processing, and computer vision. The framework provides specialized toolkits for training and deploying predictive models, including neural networks, support vector machines, and decision trees. It further distinguishes itself with deep integrations for real-time visual analysis, such as object tracking and facia

This repository provides a collection of interactive Jupyter notebooks designed to bridge theoretical machine learning concepts with practical implementation. It serves as a structured educational curriculum for deep learning, offering hands-on tutorials that guide users through the fundamentals of neural network architectures and their application. The project distinguishes itself by demonstrating identical neural network architectures across multiple industry-standard machine learning libraries, allowing for direct comparison and framework-agnostic learning. It includes utilities to transfo

This project is a PDF data extraction tool and document preprocessor designed to convert PDF files into structured formats such as Markdown, JSON, and HTML. It functions as an OCR document parser for scanned files, an accessibility automator for generating PDF/UA compliant metadata, and a loader for AI orchestration frameworks like LangChain. The software distinguishes itself through specialized handling of complex document elements, including the conversion of mathematical formulas into LaTeX and the generation of natural-language descriptions for charts and images. It utilizes recursive seg

This project is an academic curriculum repository and educational resource center for studying probability, statistics, and machine learning. It serves as a deep learning course website and a hub for instructional materials, providing a structured collection of content designed to teach neural network architectures. The repository distinguishes itself by combining a comprehensive educational resource with a machine learning project archive. It provides a curated set of research examples and implementation guides for a wide range of models, including multilayer perceptrons, convolutional netwo

This repository is a collection of guided tutorials for building and training machine learning models using the TensorFlow framework. It provides practical walkthroughs and examples for implementing a variety of model architectures to solve data prediction and analysis problems. The guides cover the construction of feedforward, convolutional, and recurrent neural networks to analyze complex data patterns. It includes specific tutorials for unsupervised learning, such as denoising autoencoders and word-to-vec embeddings, as well as examples for training generative adversarial networks to synth

NAPS2 is a suite of document scanning software consisting of a desktop application, a command-line interface tool, and a networked scanner server. It serves as an interface for capturing images from scanners via TWAIN and WIA drivers, organizing those captures into digital documents, and exporting them to various file formats. The project distinguishes itself by providing a networked scanner server that shares local hardware across a network for remote image capture. It also includes a command-line tool for automating document capture and image processing workflows through scripts and termina

Tika is a content analysis toolkit and Java library designed for detecting and extracting metadata and text from thousands of different file types. It functions as a universal document text extractor and metadata extraction engine, converting complex files into plain text or XHTML. The system employs a specialized MIME type detector that identifies document formats using magic bytes and metadata to determine the correct parser. It serves as an OCR integration gateway, connecting to external text recognition tools to extract content from image files. The project covers a broad range of extrac

This project is an educational codebase and reference library that translates theoretical deep learning concepts into executable PyTorch code. It serves as a practical implementation of a deep learning textbook, providing a course-like structure of guided exercises and architectural examples for learning purposes. The repository includes a library of standard neural network architectures, including linear, convolutional, recurrent, and transformer models. It specifically implements a variety of deep learning patterns such as multilayer perceptrons, VGG networks, gated recurrent units, and lon

This project is a collection of educational Jupyter Notebooks providing tutorials on neural network construction and tensor operations using the TensorFlow framework. It serves as a machine learning educational repository and implementation guide for deep learning students. The suite focuses on specific advanced architectures, including convolutional networks for image classification, residual networks with skip connections for training stability, and variational autoencoders for generative modeling and data synthesis. It also includes guides for building denoising and deep autoencoders to pe

PDF-Guru is an AI-powered document processor and study material converter designed to transform textbooks, research papers, and multimedia content into structured flashcards for spaced repetition systems like Anki. It functions as a content pipeline that uses language models to extract key concepts and facts from unstructured documents to generate question-and-answer pairs, cloze deletions, and multiple-choice cards. The system distinguishes itself through a comprehensive PDF management suite and multi-format parsing. It provides advanced document utilities including optical character recogni

This project is a deep learning implementation of the RetinaNet architecture for detecting and classifying objects within images. Built as a Keras object detection framework and a TensorFlow computer vision tool, it provides a complete neural network implementation based on the RetinaNet paper. The framework includes specialized components such as a Feature Pyramid Network and a focal loss function to handle object detection. It features a configurable backbone architecture and anchor-based bounding boxes to predict object locations across varying scales and aspect ratios. The toolset covers

Tensorpack is a high-level TensorFlow neural network framework and research library designed for building and training deep learning models. It provides a collection of reproducible neural network architectures for computer vision, generative tasks, reinforcement learning, and natural language processing. The project distinguishes itself through a specialized deep learning data pipeline that uses pure Python for parallel data loading and streaming. It includes a multi-GPU training orchestrator for distributing workloads via data-parallel strategies and a dedicated interpretability toolkit for