**Google AI Research Publishes 2nd Generation Optical Character Recognition Model and New Evaluation Dataset**
Google AI Research has published a new paper introducing the second generation of their Optical Character Recognition (OCR) model, as well as a new evaluation dataset for OCR models. The new model, called **Tesseract 5.0**, is a significant improvement over the previous version, Tesseract 4.0, and achieves state-of-the-art results on a variety of OCR tasks. The new evaluation dataset, called **RecogText**, is designed to be more challenging than existing OCR datasets, and provides a more realistic evaluation of OCR models.
**Tesseract 5.0**
Tesseract is an open-source OCR engine that has been used in a variety of applications, including document scanning, image processing, and text extraction. Tesseract 5.0 is a major update to the engine, and includes a number of new features and improvements, including:
* **New OCR engine:** Tesseract 5.0 uses a new OCR engine that is based on deep learning. This new engine is more accurate and efficient than the previous engine, and can handle a wider range of document types.
* **Improved pre-processing:** Tesseract 5.0 includes a number of improved pre-processing techniques that can help to improve the accuracy of the OCR engine. These techniques include image normalization, noise reduction, and skew correction.
* **New post-processing:** Tesseract 5.0 includes a number of new post-processing techniques that can help to improve the quality of the OCR output. These techniques include text recognition, language identification, and spell checking.
**RecogText**
RecogText is a new evaluation dataset for OCR models. The dataset consists of over 1 million images of text, and is designed to be more challenging than existing OCR datasets. The dataset includes a variety of document types, including books, newspapers, magazines, and handwritten notes. The dataset is also designed to be more representative of real-world OCR applications, and includes images that are noisy, skewed, and difficult to read.
Tesseract 5.0 and RecogText are both available for download on GitHub. The Tesseract 5.0 release includes a number of pre-trained models that can be used for a variety of OCR tasks. The RecogText dataset can be used to evaluate the performance of OCR models on a more challenging dataset.
**Additional Resources**
* [Tesseract 5.0 paper](https://arxiv.org/pdf/2302.12306.pdf)
* [Tesseract 5.0 GitHub repository](https://github.com/tesseract-ocr/tesseract/releases/tag/5.0.0)
* [RecogText dataset GitHub repository](https://github.com/google-research/recogtext)
**About Google AI Research**
Google AI Research is a team of researchers working on a variety of topics in artificial intelligence, including computer vision, natural language processing, and machine learning. The team’s mission is to develop new AI technologies that can help to solve real-world problems and make the world a better place.
**About Tesseract**
Tesseract is an open-source OCR engine that has been used in a variety of applications, including document scanning, image processing, and text extraction. Tesseract is developed by Google AI Research, and is available for free download on GitHub..
