
Redis
MongoDB
ArangoDB
Apache Cassandra
CouchBase
memcached
OrientDB
neo4j
DocParser
Nanonets
Parseur.com
Rossum
Docsumo
FlexiCapture
DocuClipper
Parsio.io
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache and message broker. It supports data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes with radius queries and streams. Redis has built-in replication, Lua scripting, LRU eviction, transactions and different levels of on-disk persistence, and provides high availability via Redis Sentinel and automatic partitioning with Redis Cluster.
DocParserBased on our record, Redis seems to be a lot more popular than DocParser. While we know about 237 links to Redis, we've tracked only 14 mentions of DocParser. 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.
Why a cache server? Well, to be, a cache system is the smallest piece of software one can found everywhere. There is a reason why redis, memcached or many other projects like that are used by everybody: developers need a way to store data quick. It could be for a session, for temporary data or simply to avoid annoying the main core database. A cache service is easy to create (key/value store), and can become... - Source: dev.to / 2 months ago
Adding caching layers using services like Redis cache,. - Source: dev.to / 3 months ago
Redis works well as the queue layer for this pattern. The receiver appends events to a list or stream. Workers consume from the stream, update event status on completion, and move failed events to a dead-letter queue after exhausting retries. - Source: dev.to / 3 months ago
Bifrost supports dual-layer semantic caching with exact match and semantic similarity. Backend options include Redis for exact caching, Weaviate for vector-based semantic matching, and Qdrant as an alternative vector store. - Source: dev.to / 3 months ago
In-memory caching shared across instances. There are no sticky sessions by default (though session affinity is available on a best-effort basis). Each request might hit a different instance. If you need shared state, you need an external store like Redis or Memorystore. - Source: dev.to / 4 months ago
You could try an online service like https://extract-io.web.app/ or https://docparser.com/. Source: about 3 years ago
DocParser: DocParser simplifies the extraction of structured data from various file formats, such as PDFs and scanned documents, directly into Google Sheets. By automating this process, DocParser saves valuable time and effort otherwise spent on manual data entry. Link to DocParser. Source: about 3 years ago
There are several tools available today that can help you extract tables from PDF files (such as Tabula), or even parse PDFs into structured JSON using AI (like Parsio -> I'm the founder) or without AI (like Docparser). Source: over 3 years ago
Thank you for sharing those! I didn't know them I've only checked this one https://docparser.com/ and I think my solution could be better because it will be easier for the user. Source: over 3 years ago
As previously suggested, if the layout of your PDFs never changes (consistent column widths in tables and placement), you can use a zonal PDF parser like DocParser. Alternatively, an AI-powered parser may be a better choice. Source: over 3 years ago
MongoDB - MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
Nanonets - Worlds best image recognition, object detection and OCR APIs. NanoNetsโ platform makes it straightforward and fast to create highly accurate Deep Learning models.
ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.
Parseur.com - Automate text extraction from emails and PDFs by using our powerful email and document parser.
Apache Cassandra - The Apache Cassandra database is the right choice when you need scalability and high availability without compromising performance.
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.