Amazon Comprehend
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. No machine learning experience required.
There is a treasure trove of potential sitting in your unstructured data. Customer emails, support tickets, product reviews, social media, even advertising copy represents insights into customer sentiment that can be put to work for your business. The question is how to get at it? As it turns out, Machine learning is particularly good at accurately identifying specific items of interest inside vast swathes of text (such as finding company names in analyst reports), and can learn the sentiment hidden inside language (identifying negative reviews, or positive customer interactions with customer service agents), at almost limitless scale.
Amazon Comprehend uses machine learning to help you uncover the insights and relationships in your unstructured data.
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Amazon Rekognition
Amazon Rekognition makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no machine learning expertise to use. With Amazon Rekognition, you can identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content. Amazon Rekognition also provides highly accurate facial analysis and facial search capabilities that you can use to detect, analyze, and compare faces for a wide variety of user verification, people counting, and public safety use cases.
With Amazon Rekognition Custom Labels, you can identify the objects and scenes in images that are specific to your business needs. For example, you can build a model to classify specific machine parts on your assembly line or to detect unhealthy plants. Amazon Rekognition Custom Labels takes care of the heavy lifting of model development for you, so no machine learning experience is required.
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PrecisionOCR
PrecisionOCR is a ready-to-use, secure, HIPAA-compliant, cloud-based platform for extracting medical meaning from unstructured documents using Optical Character Recognition (OCR).
PrecisionOCR uses custom Optical Character Recognition and AI algorithms to convert PDFs/JPEGs/PNGs into structured, searchable documents. Organizations can work with our team to build OCR report extractors which look for specific types of information to extract or highlight to reduce the noise that comes from extracting all of the data within a document.
Natural language processing (NLP) and machine learning (ML) power the semi-automated and automated transformation of source material such as pdfs or images into structured data records that integrate seamlessly with EMR data using HL7s FHIR standards. Data can be automatically stored along side patient records.
Our OCR document classification is also available along with multiple ways to integrate including API and CLI support.
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Google Cloud Natural Language API
Get insightful text analysis with machine learning that extracts, analyzes, and stores text. Train high-quality machine learning custom models without a single line of code with AutoML. Apply natural language understanding (NLU) to apps with Natural Language API. Use entity analysis to find and label fields within a document, including emails, chat, and social media, and then sentiment analysis to understand customer opinions to find actionable product and UX insights. Natural Language with speech-to-text API extracts insights from audio. Vision API adds optical character recognition (OCR) for scanned docs. Translation API understands sentiments in multiple languages. Use custom entity extraction to identify domain-specific entities within documents, many of which don’t appear in standard language models, without having to spend time or money on manual analysis. Train your own high-quality machine learning custom models to classify, extract, and detect sentiment.
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