NanoNets is a Deep Learning web platform that makes it easier than ever before to use Deep Learning in practical applications. It combines the convenience of a web-based platform with Deep Learning models to create image recognition and object classification applications for your business. You can easily build and integrate deep learning models using NanoNets’ API. You can also work with our pre-trained models which have been trained on huge datasets and return accurate results. NanoNets has leveraged recent advances in Deep Learning to build rich representations of data which are transferable across tasks. It’s as simple as uploading your input, generating the output and getting a functioning and highly accurate Deep Learning model for your AI needs. NanoNets is revolutionary because it allows you to train models without large datasets. With just 100 images you can train a model on our platform to detect features and classify images with a high degree of accuracy. NanoNets benefits you in four important ways: ● It reduces the amount of data needed to build a Deep Learning Model ● NanoNets handles the infrastructure for hosting and training the model, and for the run time ● It reduces the cost of running deep learning models by sharing infrastructure across models ● It is possible for anyone to build a deep learning model
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Nanonets is particularly recommended for businesses of all sizes that deal with large volumes of documents and require efficient data extraction and automation. Industries like finance, healthcare, logistics, and retail, which often handle invoices, forms, and contracts, can benefit significantly. It's also suitable for developers looking for an API solution to integrate OCR capabilities into their own applications.
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Nanonets might be a bit more popular than python pdf. We know about 6 links to it since March 2021 and only 5 links to python pdf. 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.
Please take some look in this tutorial. It is very complete and teaches you everything from installation to code. Https://realpython.com/pdf-python/. Source: about 2 years ago
But regardless of how you end up displaying it, a great first step would be to get data from the PDFs into your database. This is one of my favourite places on the web when it comes to approachable tutorials: https://realpython.com/pdf-python/. Source: about 2 years ago
To start, here’s a great article on working with PDFs in Python: Https://realpython.com/pdf-python/. Source: almost 3 years ago
How to work with PDF files with python. Source: about 3 years ago
Are you okay with paying for APIs? If so fair enough: https://ocr.space/ocrapi or browse https://rapidapi.com/marketplace for a good OCR API. As far as I know the only way to do it within python is with tesseract, which you could look into. Here's a resource on dealing with the PDF part. Source: about 4 years ago
Want to automate repetitive manual tasks? Check our Nanonets workflow-based document processing software. Source: about 3 years ago
Nanonets is a no-code, workflow-based, and AI-enhanced intelligent document processing platform. It automates all document processes and is built on a robust, intelligent, self-learning OCR API that allows users to extract required data from documents in minutes. Source: about 3 years ago
Check out our website here https://nanonets.com/ for more. We also have some free tools where you can experience our product for free (like https://nanonets.com/online-ocr). Source: about 3 years ago
Here is another company, which I just came across by accident, which do the same: https://nanonets.com/. Source: over 3 years ago
We will be using Python3.6+, Django web framework, Nanonets for character extraction from an image, Cloudinary for image storage and Google Search API for performing the searches. - Source: dev.to / over 3 years ago
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