Table of contents
Open Source
Tesseract is free and open-source, allowing developers to use, modify, and distribute the code without any cost. This makes it accessible for individual projects and startup companies.
Multiple Language Support
Tesseract supports a wide range of languages, including those with complex scripts. This makes it versatile for applications in different linguistic contexts.
Active Community
The project has an active community and is well-maintained on GitHub, which means regular updates, bug fixes, and community support are available.
High Accuracy
When properly configured and used with high-quality images, Tesseract can provide highly accurate OCR results.
Extensible
Tesseract can be integrated with other tools and frameworks, such as image pre-processing libraries, to enhance its functionality and improve OCR results.
Promote Tesseract. You can add any of these badges on your website.
Yes, Tesseract is generally considered to be a good choice for OCR tasks due to its robustness, flexibility, and the fact that it is free and open-source.
We have collected here some useful links to help you find out if Tesseract is good.
Check the traffic stats of Tesseract on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Tesseract on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Tesseract's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Tesseract on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Tesseract on Reddit. This can help you find out how popualr the product is and what people think about it.
Tesseract OCR is a powerful, free, open-source engine for converting images to text, developers use Python wrappers like pytesseract to integrate it, it's easy to use with basic coding, requiring no ML expertise, install Tesseract, then use simple functions to extract text from images, making digitization accessible, you can check it now here. - Source: dev.to / 2 months ago
Https://www.home-assistant.io/integrations/seven_segments/ https://www.unix-ag.uni-kl.de/~auerswal/ssocr/ https://github.com/tesseract-ocr/tesseract https://www.google.com/search?q=home+assistant+ocr+integration https://www.google.com/search?q=esphome+ocr+sensor https://hackaday.com/2021/02/07/an-esp-will-read-your-meter-for-you/ ...start digging around and you'll likely find something. HA has integrations which... - Source: Hacker News / 7 months ago
„OCR4all combines various open-source solutions to provide a fully automated workflow for automatic text recognition of historical printed (OCR) and handwritten (HTR) material.“ It seems to be based on OCR-D, which itself is based on - https://github.com/tesseract-ocr/tesseract - https://github.com/ocropus-archive/DUP-ocropy See - https://ocr-d.de/en/models. - Source: Hacker News / 8 months ago
Custom Integration: Developers and businesses needing flexibility for custom integration into applications and projects should consider open-source solutions like Tesseract OCR or API-based services like API4AI OCR. These options provide APIs for seamless integration into existing software systems. - Source: dev.to / about 1 year ago
Tesseract OCR is an open-source OCR engine created by Google, known for its accuracy and wide language support. It is particularly favored by developers for its flexibility and the absence of licensing fees, allowing it to be integrated into various applications. However, it demands more effort to set up and utilize compared to cloud-based OCR services. - Source: dev.to / about 1 year ago
Many of the OCR services are based on the free, open-source Tesseract OCR, but don’t expose all of the options. If you’re handy with shell scripts or Python, you can probably get better performance by hand-tuning options for your particular images. For example, if I recall there are page segmentation options to tell Tesseract to expect multi-column text. That alone might get you better performance than the... - Source: Hacker News / over 1 year ago
If you want to learn more visit the complete tesseract documentation. - Source: dev.to / over 1 year ago
AI copilots: Copilots powered by various LLMs like Pieces Copilot can leverage computer vision technologies for inputs beyond text and code. For example, optical character recognition software at Pieces uses Tesseract as its main OCR code engine, extended with bicubic upsampling. Pieces then uses edge-ML models to auto-correct any potential defects in the resulting code/text, which users can input as prompts to... - Source: dev.to / over 1 year ago
You will also need to install the Tesseract OCR engine, which can be downloaded and installed from the following link: https://github.com/tesseract-ocr/tesseract. - Source: dev.to / over 1 year ago
Tesseract is an open-source OCR engine developed by Google. It is highly accurate and supports multiple languages. This library will do all the heavy lifting for us. We'll use it in this tutorial to quickly read the text in some images. - Source: dev.to / almost 2 years ago
> Does android even have native OCR? Tesseract? https://github.com/tesseract-ocr/tesseract. - Source: Hacker News / almost 2 years ago
Install Google Tesseract OCR (additional info how to install the engine on Linux, Mac OSX and Windows). You must be able to invoke the tesseract command as tesseract. If this isn’t the case, for example because tesseract isn’t in your PATH, you will have to change the “tesseract_cmd” variable pytesseract.pytesseract.tesseract_cmd. Under Debian/Ubuntu you can use the package tesseract-ocr. For Mac OS users. Please... Source: about 2 years ago
OCR detection will be done with Tesseract. - Source: dev.to / about 2 years ago
I’ve used Tesseract for this. It seems to work well with tabular data. Https://github.com/tesseract-ocr/tesseract. Source: over 2 years ago
If you go this route, then using an app that can convert your handwritten notes to a digital format (indexed text), will give you a good balance between cognitive processing and efficient data storage/management; you can likely find many such apps on the App Store or Google Play. If you're interested in something more hands-on, on Arch you can probably experiment with Tesseract OCR in an interesting way (Example). Source: over 2 years ago
At work we use Tesseract (https://github.com/tesseract-ocr/tesseract) for OCR processing. Our workflow is to run it on images. I haven't tried it on handwriting but would definitely be interested in exploring this further. Source: over 2 years ago
I use Tesseract, I have a shortcut set to take a screenshot pass it to OCR and then put the content in my clipboard. Source: over 2 years ago
PDF format is the first part of the problem. You might be slightly better off to get scanned documents as TIFF files. In theory, you could OCR them with Tesseract, if you could install on every machine and use VBA to call the API. unfortunately, no examples. Source: over 2 years ago
I have recently discovered a few very helpful github packages which help me make notes while listening to lectures. These would be 1. Pix2tex (allows you to scan an equation and convert it to latex) 2. Pix2text (allows you to scan an equation with words in it and converts it to latex and text) 3. Tesseract (not really a physics related package, but it does allow me to copy notes from transcripts easily) 4.... Source: over 2 years ago
Use machine learning also known as magic to read the characters also known as tesseract https://github.com/tesseract-ocr/tesseract. Source: over 2 years ago
I suggest manually creating a dataset using scribd.com. It offers a free trial period of 30 days, but I am uncertain whether it covers unlimited documents or not. Nevertheless, there are over one million statements of purpose (SOPs) available on the site. You could also use the Scribd downloader. Some documents may be composed of a bunch of images, so you will have to use something like Tesseract OCR. Source: over 2 years ago
Tesseract is an open-source optical character recognition (OCR) engine widely regarded for its accuracy and adaptability, particularly in the software development and data extraction domains. Released under the Apache License, Tesseract stands out in the OCR landscape primarily because of its cost-effectiveness—it is free to use unlike many proprietary solutions such as ABBYY FineReader and Adobe Acrobat DC. As such, it is often heralded as the "best free OCR converter" across various operating systems, as highlighted in numerous recent discussions and articles, including "7 Best OCR Software of 2022" where it is acknowledged for its proficiency and precision in converting images to text.
In the competitive OCR arena, Tesseract is frequently cited as a prime alternative to more commercial OCR tools. Articles like "The Best Alternatives to Abbyy FineReader" list Tesseract as a strong contender alongside other reputable names such as Klippa DocHorizon and Nanonets, underscoring its utility for data extraction across multiple file types. Flexible integration capabilities into applications and projects make it a favorite among developers needing customizable OCR solutions, as seen in varied applications from text extraction in PDFs to advanced multimodal AI technologies.
Its public reputation is built on several key strengths. Tesseract is celebrated for its robust language support and capacity for customization through tuning of its settings, such as page segmentation for enhanced performance in specific contexts. The tool integrates well with scripting and coding ecosystems, particularly Python via the pytesseract package, thereby extending its function through automation scripts. There's a prevalent sentiment that expertise in shell scripting or Python can unlock even greater potential from Tesseract, surpassing what many cloud-based or straight-out-of-the-box OCR services offer.
However, certain challenges persist with the usage of Tesseract, particularly its steeper learning curve and the demand for more substantial setup compared to cloud-based alternatives. This aspect can pose a barrier to less technically inclined users. Furthermore, while it's highly effective with printed text, there's some interest and curiosity within the community towards exploring and possibly enhancing its capabilities for handwriting recognition—a task it isn’t natively optimized for.
In summation, while Tesseract's setup and tuning can require a degree of expertise, the cost advantages, alongside its flexibility and accuracy, continue to attract a diverse user base ranging from enthusiasts engaging in DIY projects to companies embedding OCR into comprehensive digital solutions. Its use stretches across educational settings, creative fields, and various sectors interested in leveraging OCR for improved document processing and data extraction efficiency.
Do you know an article comparing Tesseract to other products?
Suggest a link to a post with product alternatives.
Is Tesseract good? This is an informative page that will help you find out. Moreover, you can review and discuss Tesseract here. The primary details have not been verified within the last quarter, and they might be outdated. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.
