Parseur.com
DocParser
Nanonets
Docsumo
Parsio.io
Parserr
Mailparser
DocuClipper
spaCy
Amazon Comprehend
Google Cloud Natural Language API
FuzzyWuzzy
Microsoft Bing Spell Check API
OpenNLP
NLTK
PyNLPl
Parseur is a leading document processing software ranging from email parsing to PDF extraction. Use Parseur to automate text extraction from emails, PDFs, spreadsheets, attachments and documents and put your business on auto-pilot. Setup is easy as everything is point & click and intuitive. Send parsed data to thousands of applications in real time via our integrations with Google Sheets, Zapier, Microsoft Power Automate and Make or your custom application using webhooks.
Companies in finance, food delivery, real estate, e-commerce, marketing, logistics & delivery, travel, hospitality and more are saving thousands of work hours every month by automating their data entry process with Parseur.
Parseur.comWhen dealing with entities that send lots of data in an unstructured way because they think a PDF is the end of their digitalization process, Parseur is a great tool to automate reading this PDF and converting its data into structured json and then from their you can send it to your endpoint.
Email may probably never die but that doesn't mean that business processes should be slowed or halted. Parseur enables us to create a lot more efficiencies by handling email data as though it was keyed in by a customer agent.
There are other services that do this but for the low cost and the ease of use, this service is the best.
For those of us working in the European Union, Parseur was also easy to assess and approve for GDPR requirements.
The support for post processing is very powerful and with a extensive export options, it is very easy to get data into the right funnel.
Based on our record, spaCy should be more popular than Parseur.com. It has been mentiond 65 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.
You can get an account with https://parseur.com/ and then a number with OpenPhone, and Zappier. Those 3 will let you do what you want (easily). Source: over 3 years ago
Iโm sure this is super cool, but have you considered https://parseur.com itโs built for stuff like this. Source: over 3 years ago
For more complex layouts, or if you have to deal with several layouts, it may be better to use third party document extraction tool that connects to like Parseur. Source: over 3 years ago
You could use a document parser tool, like Parseur to better automate the process. Source: almost 4 years ago
And if you ever are in need of an intelligent document processing software, have a look at Parseur.com (of which I'm the co-founder, sorry for the shameless plug ;-)). Source: over 4 years ago
We use spaCyโs en_core_web_lg (Large) model as the underlying NLP engine. This gives the Redactor the linguistic context to understand that "Gatsby" in a book title should stay, but "Gatsby" mentioned as a person's name in a private letter might need to go. - Source: dev.to / 2 months ago
For NER, if accuracy is critical, go with an LLM โ even an old one like gemma-3-27b-it will outperform tools or small models trained for this task. But by using an LLM you are exposing your data, making an HTTP request, and most likely incurring a cost. If accuracy is not critical and you want to stay in Javascript, compromise is a good package for NER. If you want an even better package and it's OK not using... - Source: dev.to / 4 months ago
For more advanced food label AI, combine pattern matching with Named Entity Recognition (NER). Libraries like spaCy (Python) or compromise (JavaScript) can identify amounts, units, and nutrient names even in noisy text. - Source: dev.to / 4 months ago
For complex or highly variable menus, consider using NLP libraries like spaCy (Python) or fine-tuning a transformer-based NER model (e.g., BERT) to identify dish names and prices. - Source: dev.to / 5 months ago
Open-Source NLP Libraries: Python libraries like spaCy, NLTK, and Hugging Face Transformers for building custom models. - Source: dev.to / 6 months 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.
Amazon Comprehend - Discover insights and relationships in text
Nanonets - Worlds best image recognition, object detection and OCR APIs. NanoNetsโ platform makes it straightforward and fast to create highly accurate Deep Learning models.
Google Cloud Natural Language API - Natural language API using Google machine learning
Docsumo - Extract Data from Unstructured Documents - Easily. Efficiently. Accurately.
FuzzyWuzzy - FuzzyWuzzy is a Fuzzy String Matching in Python that uses Levenshtein Distance to calculate the differences between sequences.
