Panda vs Tesseract OCR

Overview

Panda

Stacks14

Followers28

Votes0

Tesseract OCR

Stacks96

Followers286

Votes7

GitHub Stars70.7K

Forks10.4K

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.

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CLI (Node.js)

Manual

Advice on Panda, Tesseract OCR

Vladyslav

Sr. Directory of Technology at Shelf

Oct 25, 2019

Decided

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.

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Detailed Comparison

Panda	Tesseract OCR
Panda is a cloud-based platform that provides video and audio encoding infrastructure. It features lightning fast encoding, and broad support for a huge number of video and audio codecs. You can upload to Panda either from your own web application using our REST API, or by utilizing our easy to use web interface.<br>	Tesseract was originally developed at Hewlett-Packard Laboratories Bristol and at Hewlett-Packard Co, Greeley Colorado between 1985 and 1994, with some more changes made in 1996 to port to Windows, and some C++izing in 1998. In 2005 Tesseract was open sourced by HP. Since 2006 it is developed by Google.
Unlimited encoding- When we say unlimited we mean unlimited. With your own dedicated resources, you can upload as much media as you like with no per-minute charge.;Deliver everywhere- Encode your videos to be viewable in any browser, with any player, on any device.;High definition- From the cellphone to the big screen, your video will always look gorgeous with 1080p HD video.;Broad format support- We support all of the most popular video and audio codecs including H.264, AAC, OGG, MP3, FlV, MP4 and many more;Web interface- Panda is easy for everyone with our innovative web interface that provides a straightforward process to upload, encode and monitor your content.;iPhone and iPad streaming- We support Apple HTTP Live Streaming (HLS), which dynamically adjusts the movie quality to match the speed of a connecting device.;Choose your region- Choose whether you want your video to be transferred and encoded in North America (USA) or in Europe (UK).;Supported Langyages: RUBY, PHP, PYTHON, OBJECTIVE-C, NODE.JS, MICROSOFT .NET<br>	-
Statistics
GitHub Stars -	GitHub Stars 70.7K
GitHub Forks -	GitHub Forks 10.4K
Stacks 14	Stacks 96
Followers 28	Followers 286
Votes 0	Votes 7
Pros & Cons
No community feedback yet	Pros 5 Building training set is easy 2 Very lightweight library Cons 1 Works best with white background and black text
Integrations
Heroku	No integrations available

What are some alternatives to Panda, Tesseract OCR?

Google Cloud Vision API

Google Cloud Vision API enables developers to understand the content of an image by encapsulating powerful machine learning models in an easy to use REST API.

Zencoder

Zencoder downloads the video and converts it to as many formats as you need. Every output is encoded concurrently, with virtually no waiting—whether you do one or one hundred. Zencoder then uploads the resulting videos to a server, CDN, an S3 bucket, or wherever you dictate in your API call.

Kurento

It is a WebRTC media server and a set of client APIs making simple the development of advanced video applications for WWW and smartphone platforms. Media Server features include group communications, transcoding and more.

Amazon Rekognition

Amazon Rekognition is a service that makes it easy to add image analysis to your applications. With Rekognition, you can detect objects, scenes, and faces in images. You can also search and compare faces. Rekognition’s API enables you to quickly add sophisticated deep learning-based visual search and image classification to your applications.

GStreamer

It is a library for constructing graphs of media-handling components. The applications it supports range from simple Ogg/Vorbis playback, audio/video streaming to complex audio (mixing) and video (non-linear editing) processing.

Cloudflare Stream

Cloudflare Stream makes integrating high-quality streaming video into a web or mobile application easy. Using a single, integrated workflow through a robust API or drag and drop UI, application owners can focus on creating the best video experience.

Tesseract.js

This library supports over 60 languages, automatic text orientation and script detection, a simple interface for reading paragraph, word, and character bounding boxes. Tesseract.js can run either in a browser and on a server with NodeJS.

Bacon AI

Create studio-quality images, videos, and UGC - in minutes

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Free Online Background Remover

BGRemoverFree is a smart AI tool designed to turn any image into a clean, professional visual within seconds. With a single upload, it automatically removes distracting backgrounds and highlights the main subject with perfect clarity. Whether you're preparing product photos, designing social media content, or creating marketing materials, BGRemoverFree gives you studio-quality cutouts without any editing skills. Fast, accurate, and fully web-based — it’s the easiest way to create polished, ready-to-use images for any purpose.

Related Comparisons

Panda vs Tesseract OCR: What are the differences?

Introduction

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.

Panda vs Tesseract OCR

Overview

Panda vs Tesseract OCR: What are the differences?