Scikit-learn
Pandas
NumPy
OpenCV
Dataiku
Exploratory
WEKA
htm.java
Docsumo
DocParser
Nanonets
Rossum
Parseur.com
DocuClipper
AlgoDocs
Dext
Docsumo is an intelligent document processing platform for financial services firms. Docsumo helps businesses and enterprises extract data from documents, analyze that data and detect document fraud.
Docsumoโs technology reduces back-office costs by up to 70% and increases productivity by 50%. For every million documents processed by a bank at about $1 per document, DocSumo can directly save $700k. What differentiates Docsumo is that their technology can read non-standardised documents such as bank statements, invoices, pay stubs and contracts with over 99% accuracy and more than 95% straight-through processing.
Docsumo features include:-
โ Data Capture from forms, semi-structured and unstructured financial documents โ Pre-Trained API stack for loan application, insurance compliance, invoices, supply chain management, and Commercial Real Estate applications โ Review & edit tool that allows you to click on any text in a document to capture data without manual entry โ Out of the box API endpoint (accessible via Settings page) & option to download CSV โ Multiple learning mechanism to ensure maximum accuracy โ Simple pay as you go pricing โ Ability to customize fields from the frontend โ Define templates for recurring documents โ Self-train neural network on your dataset
Choose Docsumo, if you want to:- - Automate the document data extraction end-to-end - Efficiently scale your process and your business eliminating manual data entry - Reduce risk by validating data
Scikit-learn DocsumoBased on our record, Scikit-learn seems to be a lot more popular than Docsumo. While we know about 40 links to Scikit-learn, we've tracked only 2 mentions of Docsumo. 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.
Certutil.exe or notepad.exe opening an external connection lands in rare because, fleet-wide, those processes almost never egress. Tune the <= 3 threshold to your environment size. For a more principled version, score each (process, destination) pair by frequency and treat the long tail as the hunt queue, which is the same idea behind scikit-learn's rarity-based anomaly methods without the model overhead. - Source: dev.to / about 2 months ago
Pre-configured environment. A working VM or container with Jupyter, pandas, scikit-learn, and transformers already installed. Realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. If the first hour of training is fighting CUDA installs, the course is not ready. - Source: dev.to / 2 months ago
Pre-configured environment. A good course ships a VM or container with Jupyter, pandas, scikit-learn, PyTorch or transformers, and realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. No setup tax. - Source: dev.to / 2 months ago
Isolation-based models: Build random decision trees that split features. Points that are isolated quickly (short average path length across trees) are anomalies. IsolationForest in scikit-learn implements this. Handles high-dimensional feature spaces without assuming a distribution. - Source: dev.to / 3 months ago
In practice, youโll want to use libraries (like scikit-learn or TensorFlow.js for more advanced modeling), but the principle remains: find what similar users enjoy, and use that as a basis for recommendations. - Source: dev.to / 5 months ago
Aayush here from Docsumo.com, we are a Document AI platform that empowers tech & ops teams to scale operations effortlessly by capturing, validating & analyzing unstructured documents. We recently raised $3.5 Million from Marquee investors. Source: over 3 years ago
Check out our website https://docsumo.com/ and blog https://docsumo.com/blog for more details. Source: almost 4 years ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
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.
NumPy - NumPy is the fundamental package for scientific computing with Python
Nanonets - Worlds best image recognition, object detection and OCR APIs. NanoNetsโ platform makes it straightforward and fast to create highly accurate Deep Learning models.
OpenCV - OpenCV is the world's biggest computer vision library
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.