Nanonets
DocParser
Docsumo
Rossum
DocuClipper
Parseur.com
Bank Statement Converter
Veryfi
ObjectBox
Realm.io
Microsoft SQL Server Compact
CompactView
UnQLite
VoltDB
HSQLDB
NuoDB
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
ObjectBox is a super fast database and sychronization solution, built uniquely for Mobile and IoT devices. ObjectBox is uniquely designed for small devices, so it is the ideal solution across hardware from Mobile Apps, to IoT Devices and IoT Gateways. It is the first high-performance NoSQL, ACID-compliant on-device edge database. Plus, it's built with developers in mind, with easy to use code that takes minimal time to implement.
ObjectBox supports Java, C/C++, Go, Kotlin, Swift and Python. Running on Android, Mac/iOS, Windows, Linux, Raspbian & more.
Nanonets ObjectBoxNanonets 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, ObjectBox should be more popular than Nanonets. It has been mentiond 9 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.
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: over 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
Need to sync your MongoDB database and your offline-first apps? In this tutorial, we'll walk you through setting up an end-to-end demonstration of bi-directional data sync between local ObjectBox databases on client devices and a MongoDB Atlas cluster. Together, we'll build a system that ensures offline-first functionality while keeping data in sync across devices and databases. - Source: dev.to / 8 months ago
It would be great to have the vector database run on the edge / on-device for offline-first and privacy-focused. https://objectbox.io/ does a good job of this but are there others? - Source: Hacker News / 12 months ago
When I first attempted to publish to F-Droid, I experienced several pipeline issues. After reading through the pipeline logs in GitLab, I realized that my application's database (ObjectBox) was not entirely FOSS compliant and was causing build failures. The following day was spent migrating my app to Room. - Source: dev.to / almost 3 years ago
I would focus on Kotlin instead of Java, there's really no point in sticking to Java at this point. And when it comes to databases, some local ones that are pretty easy to get into are Realm and ObjectBox, SQLite can definitely be a bit overwhelming at the beginning. Source: over 3 years ago
Just to add to this, there's also Realm and ObjectBox as alternatives. Source: over 3 years ago
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.
Realm.io - Realm is a mobile platform and a replacement for SQLite & Core Data. Build offline-first, reactive mobile experiences using simple data sync.
Docsumo - Extract Data from Unstructured Documents - Easily. Efficiently. Accurately.
Microsoft SQL Server Compact - Bring Microsoft SQL Server 2017 to the platform of your choice. Use SQL Server 2017 on Windows, Linux, and Docker containers.
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.
CompactView - Viewer for Microsoft® SQL Server® CE database files (sdf)
