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TensorFlow vs Tesseract OCR: What are the differences?
- Programming Language: TensorFlow is written in Python, while Tesseract OCR is written in C++.
- Functionality: TensorFlow is primarily used for deep learning and machine learning tasks, such as building and training neural networks, while Tesseract OCR is specifically designed for optical character recognition (OCR).
- Supported Platforms: TensorFlow is a cross-platform library that can run on various operating systems like Windows, macOS, and Linux, while Tesseract OCR is also cross-platform but can run on fewer systems, including Windows and Linux.
- Image Processing: TensorFlow provides a wide range of image processing capabilities, including image recognition, segmentation, and transformation, while Tesseract OCR is focused solely on text extraction from images and does not offer extensive image processing functionality.
- Model Training: TensorFlow offers a comprehensive framework for training machine learning models, including the ability to define and optimize the model architecture, while Tesseract OCR is designed to work with pre-trained models and does not provide built-in support for training custom models.
- Accuracy and Recognition: TensorFlow has a wider range of applications and can achieve higher accuracy in various tasks beyond OCR, while Tesseract OCR focuses specifically on text recognition and extraction and is optimized for OCR tasks.
In Summary, TensorFlow is a versatile deep learning framework that supports a variety of tasks beyond OCR, while Tesseract OCR is a specialized OCR tool primarily focused on accurate text extraction from images.
Decisions about TensorFlow and Tesseract OCR
Vladyslav Holubiev
Sr. Directory of Technology at Shelf · | 1 upvote · 46.2K views
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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Learn MorePros of TensorFlow
Pros of Tesseract OCR
Pros of TensorFlow
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
Pros of Tesseract OCR
- Building training set is easy5
- Very lightweight library2
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Cons of TensorFlow
Cons of Tesseract OCR
Cons of TensorFlow
- Hard9
- Hard to debug6
- Documentation not very helpful2
Cons of Tesseract OCR
- Works best with white background and black text1
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What is TensorFlow?
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
What is Tesseract OCR?
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.
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What companies use TensorFlow?
What companies use Tesseract OCR?
What companies use TensorFlow?
What companies use Tesseract OCR?
See which teams inside your own company are using TensorFlow or Tesseract OCR.
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What tools integrate with TensorFlow?
What tools integrate with Tesseract OCR?
What tools integrate with TensorFlow?
What tools integrate with Tesseract OCR?
No integrations found
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What are some alternatives to TensorFlow and Tesseract OCR?
Theano
Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
OpenCV
OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.