Software Alternatives, Accelerators & Startups

Keras VS DocParser

Compare Keras VS DocParser and see what are their differences

Keras logo Keras

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

DocParser logo 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.
  • Keras Landing page
    Landing page //
    2023-10-16
  • DocParser Landing page
    Landing page //
    2023-10-10

Keras videos

3. Deep Learning Tutorial (Tensorflow2.0, Keras & Python) - Movie Review Classification

More videos:

  • Review - Movie Review Classifier in Keras | Deep Learning | Binary Classifier
  • Review - EKOR KERAS!! Review and Bike Check DARTMOOR HORNET 2018 // MTB Indonesia

DocParser videos

Extract Tables From PDF to Excel, CSV or Google Sheet with Docparser

More videos:

  • Review - PDF Forms and Contracts Data Extraction - Docparser Screencast #4
  • Review - PDF Data Extraction with Docparser PDF Parser

Category Popularity

0-100% (relative to Keras and DocParser)
Data Science And Machine Learning
Data Extraction
0 0%
100% 100
Data Science Tools
100 100%
0% 0
OCR
34 34%
66% 66

User comments

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Reviews

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

Keras Reviews

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
15 data science tools to consider using in 2021
Keras is a programming interface that enables data scientists to more easily access and use the TensorFlow machine learning platform. It's an open source deep learning API and framework written in Python that runs on top of TensorFlow and is now integrated into that platform. Keras previously supported multiple back ends but was tied exclusively to TensorFlow starting with...

DocParser Reviews

We have no reviews of DocParser yet.
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Social recommendations and mentions

Based on our record, Keras should be more popular than DocParser. It has been mentiond 32 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.

Keras mentions (32)

  • Using Google Magika to build an AI-powered file type detector
    The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models. - Source: dev.to / 7 days ago
  • My Favorite DevTools to Build AI/ML Applications!
    As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development. - Source: dev.to / about 2 months ago
  • Getting Started with Gemma Models
    After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow. - Source: dev.to / 2 months ago
  • How popular are libraries in each technology
    Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks. - Source: dev.to / 12 months ago
  • Official Question Thread! Ask /r/photography anything you want to know about photography or cameras! Don't be shy! Newbies welcome!
    I'm not aware of anything off-the-shelf, but if you have sufficient programming experience, one way to do this would be to build a large dataset of reference images and pictures and use something like keras to train a convolutional neural network on them. Source: about 1 year ago
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DocParser mentions (14)

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What are some alternatives?

When comparing Keras and DocParser, you can also consider the following products

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.

Amazon Textract - Easily extract text and data from virtually any document using Amazon Textract. Textract goes beyond simple optical character recognition (OCR) to also identify the contents of fields in forms and information stored in tables.

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

FlexiCapture - ABBYY FlexiCapture brings together the best NLP, machine learning, and advanced recognition capabilities into a single, enterprise-scale platform to handle every type of document. Available in the Cloud, on premise or as SDK.

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

Docsumo - Extract Data from Unstructured Documents - Easily. Efficiently. Accurately.