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JinpengLI avatar

JinpengLI/deep_ocr

0
View on GitHub↗
1,511 stars·482 forks·Python·29 views

Deep Ocr

Deep OCR is a deep learning framework designed for the recognition of Chinese characters within images. It functions as an optical character recognition library that converts scanned documents or image files into digital text, providing an alternative to traditional template matching methods.

The system utilizes a combination of convolutional neural networks for spatial feature extraction and recurrent sequence modeling to capture contextual relationships between characters. It employs connectionist temporal classification to map image sequences to character strings without requiring explicit segmentation, and uses softmax probability decoding to determine the most likely output sequence.

The toolkit supports the development of specialized recognition engines through a transfer learning pipeline, allowing users to train models on unique font datasets. This capability facilitates the creation of custom models tailored to specific document styles, supporting workflows for automated data entry and the digitization of records.

Features

  • Chinese OCR Tools - Provides a deep learning framework for recognizing Chinese text in images with high accuracy.
  • Chinese Text Recognition - Converts images of Chinese text into digital characters using advanced machine learning models.
  • Image Recognition Engines - Identifies Chinese characters within images and converts them into digital text.
  • Optical Character Recognition - Converts images of Chinese documents into digital text using advanced neural network architectures.
  • OCR - Ships a machine learning system for training custom character recognition models using specialized font datasets.
  • Connectionist Temporal Classification - Maps image sequences to character strings using connectionist temporal classification without requiring explicit segmentation.
  • Convolutional Neural Networks - Uses deep convolutional neural network layers to extract spatial features from image pixels.
  • Font Training Pipelines - Enables the creation of specialized recognition engines by training on unique font datasets.
  • Recurrent Model Definitions - Processes image feature maps as sequential data to capture contextual relationships between characters.
  • Custom Font Adaptations - Develops specialized recognition engines by training models on unique font datasets.
  • Pre-training Transfer Learning - Adapts pre-trained weights to specific character sets to reduce training time and improve accuracy.
  • Softmax Decoders - Determines the most likely output sequence by selecting the highest probability path through the network.

Star history

Star history chart for jinpengli/deep_ocrStar history chart for jinpengli/deep_ocr

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Deep Ocr

These projects share indexed features with Deep Ocr. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • daybreak-u/chineseocr_liteDayBreak-u avatar

    DayBreak-u/chineseocr_lite

    12,324View on GitHub↗

    chineseocr_lite is a lightweight Chinese optical character recognition engine designed to detect text regions, analyze orientation, and convert Chinese characters from images into digital text. It supports both horizontal and vertical reading layouts and can be deployed as a web service for image uploads and result visualization. The system utilizes a multi-backend inference framework that supports ncnn, mnn, and tnn, allowing it to run across diverse hardware and platforms. It is specifically engineered for lightweight deployment on mobile and desktop environments through the use of small mo

    C++ncnnocrpytorch
    View on GitHub↗12,324
  • jaidedai/easyocrJaidedAI avatar

    JaidedAI/EasyOCR

    29,615View on GitHub↗

    EasyOCR is a deep learning-based computer vision library designed to perform optical character recognition on images and video frames. It functions as a comprehensive pipeline that automates the transformation of visual text into machine-readable strings, enabling the digitization of physical documents, forms, and receipts into searchable data. The engine distinguishes itself through a multi-stage processing workflow that combines convolutional neural networks for spatial feature extraction with sequence-based decoding mechanisms. This architecture allows the system to identify and interpret

    Pythoncnncrnndata-mining
    View on GitHub↗29,615
  • clovaai/deep-text-recognition-benchmarkclovaai avatar

    clovaai/deep-text-recognition-benchmark

    3,938View on GitHub↗

    This project is a PyTorch-based framework and toolkit for scene text recognition. It provides a deep learning pipeline for extracting characters and words from images of natural environments, covering the full process from training data preparation to model validation. The framework functions as a standardized benchmark for measuring the accuracy and inference speed of text recognition models. It includes tools for calculating recognition accuracy and measuring GPU processing time per image to evaluate model performance across consistent datasets. The system incorporates visual and sequentia

    Jupyter Notebook
    View on GitHub↗3,938
  • the-paperless-project/paperlessthe-paperless-project avatar

    the-paperless-project/paperless

    7,917View on GitHub↗

    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

    Python
    View on GitHub↗7,917
Compare all 30 related projects→

Frequently asked questions

What does jinpengli/deep_ocr do?

Deep OCR is a deep learning framework designed for the recognition of Chinese characters within images. It functions as an optical character recognition library that converts scanned documents or image files into digital text, providing an alternative to traditional template matching methods.

What are the main features of jinpengli/deep_ocr?

The main features of jinpengli/deep_ocr are: Chinese OCR Tools, Chinese Text Recognition, Image Recognition Engines, Optical Character Recognition, OCR, Connectionist Temporal Classification, Convolutional Neural Networks, Font Training Pipelines.

Which projects share features with jinpengli/deep_ocr?

Projects with overlapping indexed features include: daybreak-u/chineseocr_lite — chineseocr_lite is a lightweight Chinese optical character recognition engine designed to detect text regions, analyze… jaidedai/easyocr — EasyOCR is a deep learning-based computer vision library designed to perform optical character recognition on images… clovaai/deep-text-recognition-benchmark — This project is a PyTorch-based framework and toolkit for scene text recognition. It provides a deep learning pipeline… the-paperless-project/paperless — Paperless is a self-hosted document management system designed to digitize, index, and archive paper documents. It… aladdinpersson/machine-learning-collection — This project is a machine learning educational repository providing a collection of implementations and guides for… open-mmlab/mmocr — mmocr is a PyTorch-based optical character recognition framework designed for training and deploying text detection,…

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