Open Source OCR Engine – 55,000 Stars!

Tesseract Open Source OCR Engine (Main Repository)

GitHub Address

https://github.com/tesseract-ocr/tesseract

Official Website

tesseract-ocr.github.io/

Tesseract is an open-source Optical Character Recognition (OCR) engine that can recognize and extract text from image files. Tesseract was developed by Ray Smith at Hewlett-Packard’s Bristol Labs between 1985 and 1995. In 2005, Tesseract was open-sourced by HP, and it has been maintained and developed by Google since 2006.

The main features of Tesseract include:

1. Multilingual Support: Tesseract supports multiple languages, including but not limited to English, Chinese, Spanish, French, German, etc. It improves recognition accuracy by using pre-trained language models.

2. Platform Compatibility: Tesseract can run on various operating systems, including Windows, Linux, Mac OS X, etc.

3. Command Line Tool: Tesseract is primarily provided as a command line tool, allowing users to interact with it through a command line interface to perform OCR tasks.

4. Easy Integration: Tesseract can be easily integrated into other applications, providing interfaces in various programming languages such as C/C++, Python, Java, etc.

5. Open Source and Free: Tesseract is completely open source and can be used for free. Its source code is hosted on GitHub, and anyone can contribute or modify the code to suit their needs.

6. Community Support: Tesseract has an active community where users and developers can share experiences, solve problems, and improve the engine.

7. Training and Customization: Tesseract allows users to train on their own datasets to create customized language models and character recognition rules.

8. Output Formats: Tesseract supports various output formats, including plain text, HTML, PDF, TSV, etc., making it flexible for different needs.

The latest version of Tesseract is 4.x, which introduces new features and improvements, including a neural network (LSTM)-based OCR engine that focuses on line recognition and provides better character recognition performance. Tesseract 4 is also compatible with previous versions, supporting the old OCR engine mode (–oem0) and related traineddata files.

In summary, Tesseract is a powerful, flexible, and continuously evolving OCR engine with widespread applications in academia, business, and the open-source community.

Tesseract 4 adds a new neural network (LSTM)-basedOCR engine, which focuses on line recognition but still supports the old version of Tesseract OCR engine (Tesseract 3) that works by recognizing character patterns.Compatibility with Tesseract 3 can be achieved by using the old OCR engine mode (–oem 0).It also requires trained datafiles from the old engine, such as those from thetessdatarepository.

Stefan Weil is the current chief developer.Ray Smith was the chief developer until 2018. The maintainer is Zdenko Podobny.For a list of contributors, seeAuthors and GitHub’sContributors log.

Tesseractsupports Unicode (UTF-8) and can “work out of the box”to recognize over 100 languages.

Tesseract supports various image formats, including PNG, JPEG, and TIFF.

Tesseract supports various output formats: plain text, hOCR (HTML), PDF, PDF with only invisible text, TSV, and ALTO.

You should note that, in many cases, to achieve better OCR results, you need to improve thequality of theimages provided to Tesseract.

The project does not include a GUI application.If you need one, please refer to3rdParty documentation.

Tesseract can be trained to recognize other languages. For more information, seeTesseract Training.

Installation

You can install Tesseract via pre-built binary packages or build it from source.

Building Tesseract from source requires a C++ compiler with good C++17 support.

Usage

Basic command line usage:

tesseract imagename outputbase [-l lang] [--oem ocrenginemode] [--psm pagesegmode] [configfiles...]

For more information on various command line options, usetesseract --help orman tesseract.

Examples can be found in the documentation.

The use cases for the Tesseract open-source OCR engine are very broad, and it can be applied in various environments and scenarios. Here are some common use cases:

1. Document Digitization: Converting paper documents into electronic documents for easier storage, retrieval, and editing. Tesseract can recognize text in documents, enabling digitization.

2. Data Entry Automation: In scenarios where large amounts of data need to be manually entered, such as surveys, form processing, etc., Tesseract can automatically recognize and input text, improving data entry efficiency.

3. Image and Video Analysis: In image and video analysis, Tesseract can be used to extract text information from scenes, such as extracting key information from news reports, social media videos, etc.

4. Text Mining and Natural Language Processing: Tesseract can be used to extract key information from large amounts of text, supporting applications in text mining and natural language processing, such as sentiment analysis, keyword extraction, etc.

Open Source OCR Engine - 55,000 Stars!

5. Educational Assistance: In the educational field, Tesseract can be used to recognize text in test papers, handouts, and other educational materials, helping teachers and students quickly organize and review materials.

6. Financial and Insurance Industry: In the financial and insurance industry, Tesseract can be used to process various documents, such as checks, insurance policies, invoices, etc., automating the processing and verification of text information.

7. Retail and E-commerce: In the retail and e-commerce sector, Tesseract can be used to recognize product labels, barcodes, etc., supporting inventory management, price comparison, and other applications.

8. Healthcare: In the healthcare field, Tesseract can be used to recognize text in medical documents such as medical records, examination reports, etc., improving the efficiency of medical information processing.

9. Transportation and Navigation: In transportation and navigation, Tesseract can be used to recognize text information in images of road signs, traffic signs, etc., helping to improve the accuracy of navigation systems.

10. Social Media Content Analysis: On social media platforms, Tesseract can be used to recognize text in user-generated content, such as comments, posts, etc., supporting content analysis and monitoring.

11. Art and Cultural Heritage Preservation: In the field of art and cultural heritage preservation, Tesseract can be used to recognize and record text information in historical documents, descriptions of artworks, etc.

12. CAPTCHA Recognition: In scenarios where automatic CAPTCHA recognition is needed, such as automated testing, robotic programs, etc., Tesseract can be used to recognize and input text in CAPTCHAs.

The open-source nature and flexibility of Tesseract allow it to adapt to various application scenarios, and users can customize and extend it according to their needs. As technology continues to advance, the application areas of Tesseract are also expanding.

Tesseract Open Source OCR Engine (Main Repository)

GitHub Address

https://github.com/tesseract-ocr/tesseract

Official Website

tesseract-ocr.github.io/

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