# jinpengli/deep_ocr

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1,511 stars · 482 forks · Python

## Links

- GitHub: https://github.com/JinpengLI/deep_ocr
- awesome-repositories: https://awesome-repositories.com/repository/jinpengli-deep-ocr.md

## Description

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.

## Tags

### Artificial Intelligence & ML

- [Chinese OCR Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/chinese-ocr-tools.md) — Provides a deep learning framework for recognizing Chinese text in images with high accuracy.
- [Chinese Text Recognition](https://awesome-repositories.com/f/artificial-intelligence-ml/chinese-text-recognition.md) — Converts images of Chinese text into digital characters using advanced machine learning models.
- [Image Recognition Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/chinese-text-recognition/image-recognition-engines.md) — Identifies Chinese characters within images and converts them into digital text. ([source](https://github.com/jinpengli/deep_ocr#readme))
- [Optical Character Recognition](https://awesome-repositories.com/f/artificial-intelligence-ml/optical-character-recognition.md) — Converts images of Chinese documents into digital text using advanced neural network architectures.
- [Connectionist Temporal Classification](https://awesome-repositories.com/f/artificial-intelligence-ml/connectionist-temporal-classification.md) — Maps image sequences to character strings using connectionist temporal classification without requiring explicit segmentation.
- [Convolutional Neural Networks](https://awesome-repositories.com/f/artificial-intelligence-ml/convolutional-neural-networks.md) — Uses deep convolutional neural network layers to extract spatial features from image pixels.
- [Font Training Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-model-training/font-training-pipelines.md) — Enables the creation of specialized recognition engines by training on unique font datasets. ([source](https://github.com/jinpengli/deep_ocr#readme))
- [Recurrent Model Definitions](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/model-construction/neural-network-layers/recurrent-layers/recurrent-model-definitions.md) — Processes image feature maps as sequential data to capture contextual relationships between characters.
- [Custom Font Adaptations](https://awesome-repositories.com/f/artificial-intelligence-ml/ocr-model-training/custom-font-adaptations.md) — Develops specialized recognition engines by training models on unique font datasets.
- [Pre-training Transfer Learning](https://awesome-repositories.com/f/artificial-intelligence-ml/pre-training-transfer-learning.md) — Adapts pre-trained weights to specific character sets to reduce training time and improve accuracy.

### Part of an Awesome List

- [OCR](https://awesome-repositories.com/f/awesome-lists/ai/deep-learning/ocr.md) — Ships a machine learning system for training custom character recognition models using specialized font datasets.

### Scientific & Mathematical Computing

- [Softmax Decoders](https://awesome-repositories.com/f/scientific-mathematical-computing/numerical-mathematical-foundations/statistics-probability/probability-distributions/softmax-normalization/softmax-decoders.md) — Determines the most likely output sequence by selecting the highest probability path through the network.