Data Science project implementation for Online Publication Firm
Introduction
Documents in the publishing industry come in varied formats containing valuable information. The manual processing of these valuable pieces of information can be time-consuming, costly, and prone to error. With the help of AI technologies and machine learning capabilities, it is possible to easily handle document complexities and variations using intelligent document processing. In this case study, we focus on how we helped our client with the custom digitization of documents. We developed a customized solution to help our client with accurate automated data extraction from unstructured, complex documents. The resolution aimed to increase process automation and remove manual document processing bottlenecks and OCR limitations.
The Customer
The customer is a leading online publisher rooted in managing document processing for customers in different domains. Our client is a leading brand in the online publishing industry involved in various departments, including digitizing documents, creating abstracts of articles and medical journals, and categorizing articles and books according to their content.
The Challenge
- Despite the availability of reliable, scalable, and modern electronic solutions, more than 80% of document processing work is done manually.
- Imprecise abstraction of articles and journals due to common manual errors.
- Considerable time and manual effort invested in categorizing articles and journals.
- The absence of an appropriate automated system to convert paper documents into digital files resulted in insufficient data and unsatisfactory results.
- Reviewing and processing documents to find relevant information from documents was absent.
The Requirement
- Optimizing and simplifying the document processing work and gaining flexibility in automating the document digitization process.
- A platform that allows users to review documents online and conduct intelligent research based on their unique requirements.
- A platform that would eliminate manual errors by automating the abstraction of articles and journals.
- Automated categorization of the documents at scale.
- A productivity tool to assess the performance of the associates in the organization.
The Solution
Through the implementation of our data science solution powered by AI, Zlabs was able to automate the entire document processing and significantly increase the productivity of editorial workflows with an affordable and scalable SaaS.
Outcome
AI- led Document Processing
- A web-based application is created for end users to interact with excellent features.
- A data science backend engine to deliver tangible benefits across different business functions.
Document Verification with OCR
Users can upload scanned documents to the system for optical character recognition (OCR). After successful verification, the content can be fed into the system for conversion into machine-readable formats.
Effective Document Abstraction
Abstraction of information from documents will be done automatically for integration and analysis. Users can verify abstraction work and make necessary modifications if required.
Intelligent Document Analysis
The system automatically identifies topics and keywords from the document, which can be used to get a list of similar documents. In addition, an intelligent search is available to search the documents easily.
Log Management Tool
A work logging system is embedded into the web application to enable users to log their work for evaluation.
Results
Error Reduction
The OCR of Document Quality Improved by 50%.
Increased Efficiency
70% reduction in the manual work involved in creating the document's abstract.
AI-enabled Search
The intelligent search enabled users to find topics and related information very quickly.
Enhanced Productivity
The time logging system improved the productivity of employees by 30%.