Papers with Code
ML5.js
arXiv
Spell
Lobe
ML Showcase
Apple Machine Learning Journal
Amazon Machine Learning
Nanonets
DocParser
Docsumo
Rossum
Parseur.com
DocuClipper
Veryfi
Bank Statement Converter
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 NanoNetsโ 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. Itโs 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: โ It reduces the amount of data needed to build a Deep Learning Model โ NanoNets handles the infrastructure for hosting and training the model, and for the run time โ It reduces the cost of running deep learning models by sharing infrastructure across models โ It is possible for anyone to build a deep learning model
Papers with Code NanonetsNanonets 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.
Based on our record, Papers with Code seems to be a lot more popular than Nanonets. While we know about 100 links to Papers with Code, we've tracked only 6 mentions of Nanonets. 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.
Benchmark Primary focus Evaluation metrics System coverage Usability Link HumaneBench AI benchmark Human well being, humane AI principles HumaneScore, flip tests under adversarial instruction, long term well being 15 popular chat models tested across 800 realistic scenarios Designed for chatbot safety research; requires ensemble judging for... - Source: dev.to / 8 months ago
An helpful approach is to browse the state of the art models in paperswithcode. This will give you an idea of the performance of different models on various tasks. - Source: dev.to / almost 2 years ago
I think a way around this would some sort of voting/ popularity system? Papers with code (https://paperswithcode.com/) does this via Github stars sorting. Sure it doesn't mean something is established. But it at least gives some way to filter through the firehose of papers. Love this project btw! I think it has potential (and the timing is right now that everyone is looking for the next "attention is all... - Source: Hacker News / almost 2 years ago
Adapting to Evolving Standards: With the rapid progress in deep learning research and applications, staying current with the latest developments is crucial. The checklist underscores the importance of considering established standard architectures and leveraging current state-of-the-art (SOTA) resources, like paperswithcode.com, to guide project decisions. This dynamic approach ensures that projects benefit from... - Source: dev.to / about 2 years ago
Papers With Code is one of the good resources to get you to get started. - Source: dev.to / about 2 years ago
Want to automate repetitive manual tasks? Check our Nanonets workflow-based document processing software. Source: about 4 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 4 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 4 years ago
Here is another company, which I just came across by accident, which do the same: https://nanonets.com/. Source: over 4 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 / almost 5 years ago
ML5.js - Friendly machine learning for the web
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.
arXiv - arXiv is a free distribution service and an open-access archive for scholarly articles.
Docsumo - Extract Data from Unstructured Documents - Easily. Efficiently. Accurately.
Spell - Deep Learning and AI accessible to everyone
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.