FuturePerfect - Smart Document Search
Inspiration FuturePerfect aims to solve the digital organization and searchability challenges faced by museums and historical institutions. With documents and records scattered across old servers, drives, and systems dating back decades, many organizations struggle to efficiently locate and utilize these valuable archives.
FuturePerfect offers a consolidated platform to bring together dispersed digital artifacts into one searchable collection. Our smart indexing models can extract text, metadata, and visual data from even the most unstructured digitized records. This enables intelligent full-text search across various file formats like scanned images, PDFs, office documents, audio, and more.
With FuturePerfect, historians and researchers can instantly find relevant records by searching across the full collection using dates, names, locations, keywords and more. Searches become precise and effective instead of frustrating fishing expeditions. Deep learning relevance algorithms surface the most useful records.
Beyond great search, FuturePerfect also facilitates intuitive organization and discovery of connections in the archives. Smart tagging based on entities, dates, and visual concepts provides a base taxonomy that can be collaboratively enhanced by experts. Relationships between records become visible.
By making disorganized digital archives searchable and structured, FuturePerfect aims to help museums unlock hidden insights and knowledge from their rich histories. Our goal is to make historical records come alive and tell their stories better.
What We Learned Building FuturePerfect taught us a tremendous amount about full-text search, natural language processing, and machine learning for documents. we learned how to: Data Processing and Storage: We learned how to efficiently handle a variety of data types, including text, images, audio, and more. We explored methods for data extraction, transformation, and storage, tailoring our approaches to each data type's unique characteristics. We also learned many features around AWS like IAM roles, policies, S3 bucket security, lambda, ec2 instance, dynamoDB database.
How we Built It We built FuturePerfect using Python, Django, Angular, and AWS services: -Django REST API for backend -AngularJS frontend for intuitive search UI -Backend Machine Learning Model to parse/index uploads -S3 for storage -DynamoDB NoSQL Service for indexing and query -Pytesseract, yake(Yet Another Keyword Extractor) for ML, Aspose.words for getting keywords from the files. -Created IAM role with specific set of policies of admin in AWS and manges S3, dynamoDB services with security. The main challenge was piecing all these elements together into a cohesive product.
Challenges Below are some challenges that we faced during hackathon: -Less experience with Cloud Architecture -Creating complex indexing and queries to seach on NoSQL database -Integrating image and audio text extraction functionalities with Django API
What's Next Looking forward, I'm excited to explore: -Provide admin access for uploads only -Integrate audio, video and image scripts to Django APIs -Facilitate Multiupload functionality -Add more security for the uploaded data
FuturePerfect has come a long way but still has so much potential! Excited to see where it goes next.
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