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Panda vs Tesseract OCR: What are the differences?
Introduction
Panda and Tesseract OCR are two popular tools used for Optical Character Recognition (OCR) in different applications. While both aim to recognize and extract text from images or documents, there are several key differences between the two.
Language Support: Panda OCR supports multiple languages including English, Spanish, French, German, and more. On the other hand, Tesseract OCR provides support for a wide range of languages, with over 100 languages available.
Accuracy: Tesseract OCR is known for its high accuracy in recognizing text from images or scanned documents. It uses an advanced algorithm and machine learning techniques to achieve accurate results. Panda OCR, although providing decent accuracy, may not be as accurate as Tesseract OCR in complex cases or with low-quality images.
Ease of Use: Panda OCR offers a user-friendly interface, making it easy for users to integrate OCR functionality into their applications with minimal coding effort. Tesseract OCR, while providing powerful OCR capabilities, requires more technical expertise and coding knowledge to implement.
Image Preprocessing: Tesseract OCR requires additional pre-processing steps to improve the accuracy of the OCR results. This may include image enhancement techniques such as noise reduction, contrast adjustment, or skew correction. Panda OCR, on the other hand, incorporates these pre-processing steps as part of its OCR engine, eliminating the need for additional pre-processing.
Speed: Tesseract OCR is known for its fast processing speed, making it suitable for applications that require real-time or near-real-time OCR. Panda OCR, while offering reasonable speed, may not be as fast as Tesseract OCR in processing large volumes of images or documents.
Community Support: Tesseract OCR has a vibrant and active community of developers, contributing to its continuous improvement and development. It benefits from regular updates and bug fixes. Panda OCR, while also having community support, may not have the same level of activity or extensive documentation as Tesseract OCR.
In summary, Panda and Tesseract OCR have key differences in language support, accuracy, ease of use, image preprocessing, speed, and community support. Each tool has its strengths and weaknesses, and the choice depends on the specific requirements and use cases of the application.
AWS Rekognition has an OCR feature but can recognize only up to 50 words per image, which is a deal-breaker for us. (see my tweet).
Also, we discovered fantastic speed and quality improvements in the 4.x versions of Tesseract. Meanwhile, the quality of AWS Rekognition's OCR remains to be mediocre in comparison.
We run Tesseract serverlessly in AWS Lambda via aws-lambda-tesseract library that we made open-source.
Pros of Panda
Pros of Tesseract OCR
- Building training set is easy5
- Very lightweight library2
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Cons of Panda
Cons of Tesseract OCR
- Works best with white background and black text1
