Generalized extraction of Embedding Vectors from unstructured data
This is a generic solution for extracting features in the form of Embedding vectors from any unstructured data including images, videos, text and speech data.
It enables scalable similarity based search and gain insights from large volumes of unstructured data.
Our solution can work with data stored in a Cloud Data Lake on GCP Cloud Storage or AWS S3, and store the resulting Embeddings as either raw or compressed Embedding vectors in the same Data Lakes.
Similarity based search of Embedding vectors
Similarity-based, distributed, and massively scalable search is a critical tool for businesses seeking to analyze large volumes of data efficiently.
DreamAI has key building blocks to shorten the time to market for converting raw Embedding Vectors into large, distributed search indexes on Cloud platforms, and then perform parallel, similarity-based searches in these search indexes. This enables businesses to identify relationships and patterns within their data quickly and accurately.
This capability also enables businesses to improve their data management, ensuring that they can quickly and accurately access the information they need to succeed.
By leveraging these solutions, businesses can improve their operations, reduce costs, and gain a competitive advantage in the market.
Computer Vision: Image Classification
This is a crucial module that enables businesses to automatically categorize and organize their image data. Our solution enables leveraging pre-trained models or developing custom models fine-tuned to a company’s specific data tailored to their specific needs.
The solution supports distributed training on multiple GPUs on all major Cloud Platforms. It also supports deployment in batch and online prediction modes.
Computer Vision: Video Action Classification
Our Video classification module enables businesses to automatically categorize and organize their video data. It can classify segments of a large video, identifying the specific action that takes place in each segment. This has several use cases ranging from sports analytics to security and surveillance.
Enterprises can use our pre-trained models or bring their own videos and train and deploy their custom models in a scalable manner on any Cloud or on-premises setting. The solution supports distributed training on multiple GPUs on all major Cloud Platforms. It also supports deployment in batch and online prediction modes.
Computer Vision: Image captioning
Image captioning is a powerful application of artificial intelligence that enables businesses to automatically generate captions for their image data.
Our Image captioning solution works with both image and video data. It can generate accurate captions for user-supplied images and video clips.
Businesses can also fine tune their custom models if there are any special needs for captions that could not be met with our pretrained models.The solution supports distributed training on multiple GPUs on all major Cloud Platforms. It also supports deployment in batch and online prediction modes.
Note: Image Source: https://github.com/danieljl/keras-image-captioning/raw/master/results-without-errors.jpg
Computer Vision: Object detection
Object detection enables businesses to automatically detect and localize objects within their image or video data.
This has several use cases ranging from people counting to identifying tumors in medical images to detecting objects of interest in self-driving cars.
It is also used in sports analytics, smart cities and anomaly detection in manufacturing processes.The solution supports distributed training on multiple GPUs on all major Cloud Platforms. It also supports deployment in batch and online prediction modes.
Note: Image Source: https://www.flickr.com/photos/drbeachvacation/35477618781
Computer Vision: Semantic segmentation
Semantic segmentation enables businesses to automatically segment and classify different regions within their image data.
This has several use cases very similar to object detection such as people counting to identifying tumors in medical images to detecting objects of interest in self-driving cars, sports analytics etc. However, segmentation is much more precise and accurate than object detection at the cost of being more resource intensive.
The solution supports distributed training on multiple GPUs on all major Cloud Platforms. It also supports deployment in batch and online prediction modes.
Computer Vision: Depth and distance measurement in videos
Depth and distance measurement for videos module automatically measures the depth and distance between objects within a given video clip.
It can serve as a key building block in a larger solution such as SportsAnalytics, People Counting for seminars and events, smart cities and surveillance applications etc.
The solution supports deployment in batch and online prediction modes.
Computer Vision: Person and object tracking in videos
Person and object tracking for videos module tracks individuals and objects in their video data.
It can serve as a key building block in a larger solution such as SportsAnalytics, People Counting for seminars and events, smart cities and surveillance applications etc.
Computer Vision: Motion and Optical Flow estimation in videos
Motion and optical flow estimation module for videos automatically measures the motion and direction of objects within video data.
It has significant applications in Sports Analytics, self-driving cars, Smart cities and surveillance, Super Resolution for Videos and many others.
Document processing and Text Extraction
This module automatically extracts and analyzes text data from various types of documents.
Using this module, businesses can quickly and accurately extract valuable information from their documents, improving their data management and analysis capabilities.
This capability also enables businesses to automate routine tasks and free up staff to focus on higher-value activities, ultimately improving operational efficiency and profitability.
Resume processing for Human Resources
Resume processing module is a critical building block of a larger human resources system.
It uses Machine Learning to automatically extract and analyze information from resumes and other job application documents. This solution allows businesses to streamline their hiring processes and improve their recruitment strategies.
By leveraging our resume processing solution, businesses can quickly and accurately extract valuable information from job applications, such as candidate skills, experience, education, and more. This capability enables HR departments to automate routine tasks and free up staff to focus on higher-value activities, ultimately improving their recruitment processes and the quality of their hires.
Question and Answering on business Knowledge Databases
This is a critical solution that enables companies to extract meaningful insights and valuable information from their data based on simple, question/answering. We work with our customers to convert their data into knowledge graphs and store them in a graph database like Neo4j. The Knowledge Graph data could then be queried through a text-based question/answer interface.
The solution pipeline consists of an intent classification, information retrieval, Named Entity Recognition and a Cypher language query constructor module that fills the query templates with extracted entities and information.
The solution uses knowledge graphs to connect information across multiple data sources, enabling users to find answers to their questions quickly and easily. It helps companies improve their decision-making processes, optimize their operations, and gain a competitive advantage in their industry.
Face Recognition from large databases
This solution uses Machine Learning algorithms to identify and verify the identity of individuals based on their facial features.
With the ability to quickly and accurately match faces to large databases of images, this solution can be used in a range of industries, from security and law enforcement to marketing and retail. It is a highly scalable, Cloud based solution and uses advanced Deep Learning based algorithms to analyze facial features and patterns.
It can be trained in scalable manner on multiple GPUs on customer’s facial image databases and supports deployment in batch and online prediction modes.
License Plate Recognition
Our License plate recognition (LPR) solution uses artificial intelligence and computer vision to capture and recognize license plate numbers on vehicles.
LPR systems can be used for a variety of applications, including parking management, toll collection, traffic monitoring, and law enforcement.
Our solution leverages the power of cloud platforms to perform fast and accurate recognition of license plate numbers from images and videos. It can be trained in scalable manner on multiple GPUs on customer’s facial image databases and supports deployment in batch and online prediction modes.
Speech Processing and Classification
Our Speech processing and classification solution uses artificial intelligence and Deep Learning algorithms to analyze, understand and classify speech data. This includes tasks such as speech recognition, speaker identification, emotion detection, language translation, and more.
Our solution leverages the power of cloud platforms to perform scalable and accurate analysis of speech data. It can accurately identify and classify speech data in real-time, even when dealing with large datasets.
It can be trained in scalable manner on multiple GPUs on customer’s facial image databases and supports deployment in batch and online prediction modes.
Speech To Text and Text To Speech
This solution incorporates two important applications of speech technology that involve the conversion of spoken language into written text or synthesized speech, respectively.
Speech-to-text module uses machine learning algorithms to convert audio recordings or live speech into text format, both in batches and in real time. This solution can be used in industries such as healthcare, education, customer service, and more, where it is used to transcribe audio notes, generate captions for videos, and automate the process of generating text from audio recordings.
Text-to-speech module uses artificial intelligence and machine learning to convert written text into synthesized speech. This can be used to generate audio versions of text documents, provide audio feedback for customer service applications, generating automated voice announcements, cloning author’s voice for automated creation of audio books and more.
It can be trained in scalable manner on multiple GPUs on customer’s facial image databases and supports deployment in batch and online prediction modes.
Note: Image Source: https://www.pxfuel.com/en/free-photo-jrcvh
