Software Alternatives, Accelerators & Startups

Rossum VS TensorFlow

Compare Rossum VS TensorFlow and see what are their differences

Rossum logo Rossum

Rossum is AI-powered, cloud-based invoice data capture service that speeds up invoice processing 6x, with up to 98% accuracy. It can be easily customized, integrated and scaled according to your company needs.

TensorFlow logo TensorFlow

TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
  • Rossum Landing page
    Landing page //
    2023-08-24
  • TensorFlow Landing page
    Landing page //
    2023-06-19

Rossum features and specs

  • High Accuracy
    Rossum's AI engine is known for its high accuracy in extracting data from various types of documents, reducing the need for manual corrections.
  • Scalability
    The platform is highly scalable, making it suitable for businesses of all sizes, from startups to large enterprises.
  • Integrations
    It offers seamless integration with popular ERP, CRM, and other business systems, facilitating smooth workflows.
  • Time Savings
    Automating data extraction processes saves significant time for employees, allowing them to focus on more value-added tasks.
  • User-Friendly Interface
    The platform has a user-friendly interface that makes it easy for employees to manage and validate data.
  • Multi-Language Support
    Rossum supports multiple languages, making it a versatile tool for international businesses.

Possible disadvantages of Rossum

  • Cost
    The pricing can be relatively high for small businesses or startups with limited budgets.
  • Initial Setup
    The initial setup and training period can be time-consuming, requiring significant effort to integrate the system fully.
  • Learning Curve
    Despite the user-friendly interface, there is still a learning curve associated with mastering all features and functionalities.
  • Dependency on Internet
    Being a cloud-based solution, a stable internet connection is essential for uninterrupted service, which could be a limitation in areas with poor connectivity.
  • Customization Limitations
    While it offers many features, there might be specific customization needs that are not easily met by the platform.

TensorFlow features and specs

  • Comprehensive Ecosystem
    TensorFlow offers a complete ecosystem for end-to-end machine learning, covering everything from data preprocessing, model building, training, and deployment to production.
  • Community and Support
    TensorFlow boasts a large and active community, as well as extensive documentation and tutorials, making it easier for beginners to learn and experts to get help.
  • Flexibility
    TensorFlow supports a wide range of platforms such as CPUs, GPUs, TPUs, mobile devices, and embedded systems, providing flexibility depending on the user's needs.
  • Integrations
    TensorFlow integrates well with other Google products and services, including Google Cloud, facilitating seamless deployment and scaling.
  • Versatility
    TensorFlow can be used for a wide range of applications from simple neural networks to more complex projects, including deep learning and artificial intelligence research.

Possible disadvantages of TensorFlow

  • Complexity
    TensorFlow can be challenging to learn due to its complexity and the steep learning curve, particularly for beginners.
  • Performance Overhead
    Although TensorFlow is powerful, it can sometimes exhibit performance overhead compared to other, lighter frameworks, leading to longer training times.
  • Verbose Syntax
    The code in TensorFlow tends to be more verbose and less intuitive, which can make writing and debugging code more cumbersome relative to other frameworks like PyTorch.
  • Compatibility Issues
    Frequent updates and changes can lead to compatibility issues, requiring significant effort to keep libraries and dependencies up to date.
  • Mobile Deployment
    While TensorFlow supports mobile deployment, it is less optimized for mobile platforms compared to some other specialized frameworks, leading to potential performance drawbacks.

Analysis of Rossum

Overall verdict

  • Yes, Rossum is generally considered a good solution for businesses looking to streamline their document processing tasks. Its user-friendly interface and robust AI capabilities make it a popular choice among companies aiming to automate their data extraction processes.

Why this product is good

  • Rossum provides an AI-driven platform for automating document processing. It is well-regarded for its ability to efficiently extract information from various document types, reducing the need for manual data entry and improving productivity. The platform leverages machine learning and customizable workflows to adapt to the specific needs of different industries and document formats, enhancing accuracy and speed.

Recommended for

  • Businesses with high volumes of document processing needs
  • Companies seeking to automate their data extraction and reduce manual entry errors
  • Industries such as finance, logistics, healthcare, and insurance that deal with standardized documents
  • Organizations looking to implement AI-driven solutions to improve operational efficiency

Rossum videos

Intro & Overview w/ Rossum Electro-Music Assimil8or Eurorack Sampler Module

More videos:

  • Review - Rossum Evolution 1/4: Overview (LMS Eurorack Expansion Project)
  • Review - Rossum Electro-Music Trident // Triple VCO with UNIQUE Analog Tones & Modulation

TensorFlow videos

What is Tensorflow? - Learn Tensorflow for Machine Learning and Neural Networks

More videos:

  • Tutorial - TensorFlow In 10 Minutes | TensorFlow Tutorial For Beginners | Deep Learning & TensorFlow | Edureka
  • Review - TensorFlow in 5 Minutes (tutorial)

Category Popularity

0-100% (relative to Rossum and TensorFlow)
OCR
100 100%
0% 0
Data Science And Machine Learning
Data Extraction
100 100%
0% 0
AI
43 43%
57% 57

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Rossum and TensorFlow

Rossum Reviews

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TensorFlow Reviews

7 Best Computer Vision Development Libraries in 2024
From the widespread adoption of OpenCV with its extensive algorithmic support to TensorFlow's role in machine learning-driven applications, these libraries play a vital role in real-world applications such as object detection, facial recognition, and image segmentation.
10 Python Libraries for Computer Vision
TensorFlow and Keras are widely used libraries for machine learning, but they also offer excellent support for computer vision tasks. TensorFlow provides pre-trained models like Inception and ResNet for image classification, while Keras simplifies the process of building, training, and evaluating deep learning models.
Source: clouddevs.com
25 Python Frameworks to Master
Keras is a high-level deep-learning framework capable of running on top of TensorFlow, Theano, and CNTK. It was developed by François Chollet in 2015 and is designed to provide a simple and user-friendly interface for building and training deep learning models.
Source: kinsta.com
Top 8 Alternatives to OpenCV for Computer Vision and Image Processing
TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks such as machine learning, computer vision, and natural language processing. It provides excellent support for deep learning models and is widely used in several industries. TensorFlow offers several pre-trained models for image classification, object detection,...
Source: www.uubyte.com
PyTorch vs TensorFlow in 2022
There are a couple of notable exceptions to this rule, the most notable being that those in Reinforcement Learning should consider using TensorFlow. TensorFlow has a native Agents library for Reinforcement Learning, and Deepmind’s Acme framework is implemented in TensorFlow. OpenAI’s Baselines model repository is also implemented in TensorFlow, although OpenAI’s Gym can be...

Social recommendations and mentions

Based on our record, TensorFlow should be more popular than Rossum. It has been mentiond 7 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Rossum mentions (4)

  • Data management program/software
    Embrace the AI bubble: https://rossum.ai/ (I'm not affiliated). Source: almost 2 years ago
  • [HIRING] Python OCR help (freelance help)
    Now my main point (no, not IBM cloud services !) An other way is desktop tool/cloud tool that are OCR dedicated to "formatted documents" like ROSSUM or KLIPPA and... (https://rossum.ai/, https://www.klippa.com/en/ocr/identity-documents/driving-licenses). The idea, if I remember well the business model, is like a lot of small companies need all to make OCR on the same type of documents you can pre-learn an IA then... Source: over 2 years ago
  • [D] OCR models for invoice reading
    You should check out https://rossum.ai/ I think their product fits your usecase. Source: almost 3 years ago
  • how to create universal regex which can extract lot of data from multiple invoices in python.
    I have seen some site like https://rossum.ai/ and while I think it is very difficult is there a way to improve it like them ? Source: almost 4 years ago

TensorFlow mentions (7)

  • Creating Image Frames from Videos for Deep Learning Models
    Converting the images to a tensor: Deep learning models work with tensors, so the images should be converted to tensors. This can be done using the to_tensor function from the PyTorch library or convert_to_tensor from the Tensorflow library. - Source: dev.to / over 2 years ago
  • Need help with a Tensorflow function
    So I went to tensorflow.org to find some function that can generate a CSR representation of a matrix, and I found this function https://www.tensorflow.org/api_docs/python/tf/raw_ops/DenseToCSRSparseMatrix. Source: almost 3 years ago
  • Help: Slow performance with windows 10 compared to Ubuntu 20.04 with TF2.7
    Can anyone offer up an explanation for why there is a performance difference, and if possible, what could be done to fix it. I'm using the installation guidelines found on tensorflow.org and installing tf2.7 through pip using an anaconda3 env. Source: about 3 years ago
  • [Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
    I don't have much experience with TensorFlow, but I'd recommend starting with TensorFlow.org. Source: about 3 years ago
  • [Question] What are the best tutorials and resources for implementing NLP techniques on TensorFlow?
    I have looked at this TensorFlow website and TensorFlow.org and some of the examples are written by others, and it seems that I am stuck in RNNs. What is the best way to install TensorFlow, to follow the documentation and learn the methods in RNNs in Python? Is there a good tutorial/resource? Source: about 3 years ago
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What are some alternatives?

When comparing Rossum and TensorFlow, you can also consider the following products

DocParser - Extract data from PDF files & automate your workflow with our reliable document parsing software. Convert PDF files to Excel, JSON or update apps with webhooks.

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Nanonets - Worlds best image recognition, object detection and OCR APIs. NanoNets’ platform makes it straightforward and fast to create highly accurate Deep Learning models.

Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

Veryfi - Bookkeeping automation with AI/machine powered end-to-end

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.