Projects sharing features with Deep Ocr

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

This project is a machine learning educational repository providing a collection of implementations and guides for machine learning and deep learning algorithms. It serves as a deep learning model library and a reference for training workflows, covering foundational machine learning, convolutional, recurrent, and transformer architectures. The collection includes a generative adversarial network suite for synthesizing realistic images and performing image-to-image translation. It also functions as a computer vision implementation guide for object detection and semantic segmentation, alongside

mmocr is a PyTorch-based optical character recognition framework designed for training and deploying text detection, recognition, and key information extraction models. It serves as a comprehensive toolbox for scene text detection and recognition, providing specialized libraries for locating text regions and converting visual text into machine-encoded strings. The project distinguishes itself through a research framework for key information extraction and advanced text spotting capabilities. These include point-based spotting using transformers and the use of parameterized Bezier curves to id

Tesseract is an optical character recognition engine and tool designed to convert printed or handwritten text from images into machine-readable digital text. It functions as a multilingual text extractor and a document digitization pipeline that transforms scanned images into structured digital formats. The project includes a framework for training custom scripts and language-specific models, allowing the engine to recognize new languages or unique fonts through custom training data. Its capabilities cover automated text extraction, digital archive digitization, and the export of recognized

WriteGPT is an end-to-end essay automation system that combines visual recognition and automated text generation to convert images into finished digital documents. It functions as a creative text generator and document processor, utilizing language models to produce long-form written content and essays. The system integrates a neural text fluency evaluator to score the linguistic quality and naturalness of generated prose. It also includes a transformer-based text summarizer to condense long documents into concise summaries. The project provides a pipeline for optical character recognition t

This project is a collection of TensorFlow machine learning examples providing reference implementations for various neural network paradigms. It covers supervised, unsupervised, reinforcement, and sequential learning models. The repository includes implementations for convolutional neural networks focused on image classification and ranking, as well as recurrent neural networks for time-series forecasting and sequence-to-sequence translation. It further provides examples of reinforcement learning agents trained via reward optimization and unsupervised learning techniques such as autoencoders

This repository provides the pre-trained neural network and legacy data files used by Tesseract to recognize and extract printed text from images. It serves as a multilingual training data repository and a collection of Long Short-Term Memory models designed for high-accuracy optical character recognition across various global scripts and languages. The data includes specialized models for analyzing image layouts to determine text rotation and script direction. It provides the necessary language-specific datasets and linguistic patterns required to enable Tesseract OCR engines to function. T

X-AnyLabeling is an AI-assisted annotation platform and computer vision labeling tool. It provides an interface for annotating images and videos using polygons and rectangles to create training sets for machine learning models. The project distinguishes itself through the integration of external AI models via a plugin-based inference backend, allowing for automated generation of candidate labels and the execution of specialized tasks like pose estimation and object detection. It also functions as an optical character recognition tool for extracting text and layout information from document im

This project is a PyTorch implementation of a U-Net convolutional neural network designed for pixel-level image segmentation. It functions as a biomedical image processor that generates precise masks to isolate anatomical structures within medical imagery. The architecture utilizes a symmetric encoder-decoder structure to capture context and enable precise localization. It employs skip-connection feature fusion to combine high-resolution features from the contracting path with upsampled outputs, recovering spatial detail. The system covers deep learning model training using binary cross-entr

This repository collects illustrated single-page cheat sheets that compress the core topics of Stanford's CS 230 deep learning course into visual reference summaries. The collection covers convolutional neural networks, recurrent neural networks, and practical training techniques, pairing schematic diagrams with mathematical notation to bridge intuition and formal understanding. The cheat sheets are organized by subject area and link related concepts across topics, such as connecting vanishing gradients to LSTM gates, to reinforce the full deep learning workflow. Practical training advice on

Parsr is an unstructured data extractor and document parsing pipeline that converts raw files and images into cleaned, machine-readable formats. It functions as a document layout analyzer and a pipeline for extracting structured data and labels using large language models. The system includes a document parsing visualizer, providing a graphical interface to upload documents and inspect the resulting structured data output. The project covers document digitization workflows, including layout analysis to detect headings, tables, and lists, and automated data entry through the cleaning and enri

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

Camelot is a Python library and processing engine designed to extract tabular data from PDF documents. It converts unstructured tables into machine-readable formats such as CSV, JSON, and Excel. The project provides specialized toolsets for different document types, using line detection for ruled tables and whitespace analysis for borderless tables. It includes an optical character recognition system to recover structured data from image-based scanned PDFs that lack a digital text layer. The library handles complex document layouts, including encrypted files, rotated pages, and tables that s

chineseocr is an end-to-end deep learning pipeline for detecting and recognizing Chinese and English text in images. The project combines text region detection using YOLOv3 with sequence-based recognition via Convolutional Recurrent Neural Networks (CRNN) and dense OCR models, forming a complete optical character recognition workflow. The pipeline includes orientation detection to handle text rotated at 0, 90, 180, or 270 degrees before recognition, and supports structured field extraction from identity cards and train tickets. A multi-framework model converter enables trained models to be co

This project is an optical character recognition tool designed to extract hardcoded subtitles from video frames and convert them into synchronized subtitle files. It functions as a text processor that transforms embedded visual text into a written format to improve video accessibility and translation. The system uses graphics processing units to increase the speed and accuracy of text recognition. It includes a subtitle cleaning tool that applies custom mapping configurations to filter out watermarks, channel logos, and duplicate lines from the extracted text. The tool supports batch process

This application is a cross-platform desktop utility designed for automated translation, optical character recognition, and speech synthesis. It functions as a modular client that integrates various local and remote language services, allowing users to process text through hotkeys, clipboard monitoring, or direct input. The software distinguishes itself through a plugin-based architecture and a built-in automation framework. By exposing a local network interface, it enables external applications and scripts to programmatically trigger its translation and recognition workflows. Users can furth

Umi-OCR is an optical character recognition engine designed to convert visual text from images and documents into machine-readable character data. It functions as a local-first toolkit, processing all visual data directly on the host machine using embedded neural network models to maintain privacy and offline availability. The project distinguishes itself through its focus on automated document digitization and integrated barcode and QR code decoding. By utilizing a modular, Python-based orchestration layer, it enables users to transform static image files and multi-page documents into search

DeepSeek-OCR is a vision processing framework designed to convert image-based text into machine-readable tokens for large language models. It functions as a document inference pipeline that encodes visual data into compact representations, enabling automated optical character recognition and document analysis workflows. The system distinguishes itself through a high-throughput architecture that utilizes hardware-accelerated batch inference to process large volumes of visual data. It incorporates dynamic resolution scaling to manage the balance between visual detail and token consumption, ensu

PDF-Extract-Kit is a document extraction toolkit designed to convert PDF documents into structured formats such as Markdown, HTML, and LaTeX. It functions as a multi-stage parsing framework that combines a document layout analyzer, a formula recognition engine, an OCR text extractor, and a table extraction system. The project focuses on recovering complex document elements by translating images of mathematical formulas and tabular structures into editable source code. It utilizes model-driven layout analysis to identify structural elements in reports and textbooks while ignoring noise like wa

Dango-Translator is an OCR translation system and multi-engine translation client designed to extract text from images or screens and replace it with translated content. It functions as an image text translator and real-time screen translator, utilizing optical character recognition to convert text between different languages automatically. The software distinguishes itself through coordinate-based image typesetting and a glossary manager. These tools allow for the replacement of original image content with translated text in the same area and the use of specialized dictionaries to ensure con

SwiftOCR is a Swift library for performing optical character recognition and text extraction from images. It functions as a neural network text recognizer and OCR model trainer designed for iOS and macOS applications. The project provides tools for custom font training, allowing users to teach neural networks to recognize specific typography or unique character sets by processing custom datasets. This enables the system to identify short alphanumeric sequences based on specific target character mappings. The library includes an image-preprocessing pipeline to clean and transform visual data

Textract is a multi-format text extraction tool and parser. It provides a unified interface to extract plain text from a variety of sources, including documents, images, and audio files. The system functions as a document content parser for PDFs and spreadsheets, an image text extractor using optical character recognition, and a speech-to-text transcriber for audio recordings.

This project is a CAPTCHA solver browser extension that automatically detects and resolves image, text, and behavioral challenges using an AI inference engine. It functions as a bot detection bypass tool designed to overcome interactive web barriers and session timeouts to maintain access to protected websites. The extension provides a bridge between automated solving capabilities and external programming languages or browser automation frameworks via an API integration. It utilizes an AI-powered optical character recognition system to transcribe text from images and auditory challenges into

EasyPR is an automatic license plate recognition system designed to detect vehicle license plates and extract alphanumeric characters from images of Chinese vehicles. It functions as a deep learning OCR tool that converts image regions of license plates into machine-readable text strings. The system includes a specialized detector for identifying vehicle plates within unconstrained environments and complex visual backgrounds. It also provides a synthetic data generator to create artificial image datasets used to train and improve the accuracy of the recognition models. The project covers a m

Botasaurus is a Python web scraping framework and headless browser automation system used to build scalable data extraction tools. It functions as a web data extraction tool and OCR document parser, converting website content, images, and PDF files into structured formats such as JSON, CSV, and Excel. The framework distinguishes itself by providing a scraper management interface that allows Python functions to be wrapped in a web-based UI or deployed as standalone desktop applications. This enables non-technical users to trigger extraction jobs and manage tasks via a graphical interface or RE

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

Kreuzberg is a document extraction engine that converts PDFs, Office files, images, and over 90 other formats into clean, structured text and metadata. It is built around a compiled Rust core that can be used as a native library, a command-line tool, a REST API server, or a WebAssembly module for browser-based processing. The system is designed to run entirely on self-hosted infrastructure, with no data leaving the user's environment. What distinguishes Kreuzberg is its breadth of integration surfaces and its pipeline architecture. It exposes extraction capabilities through native bindings fo