Affinda is trusted by some of the world's leading organisations
Want to try
the free tool?
What is a Letter of Recommendation OCR Data Extractor?
A Letter of Recommendation Optical Character Recognition (OCR) data extractor is an innovative and smart technology tool that uses Artificial Intelligence (AI) to interpret and extract vital information from Letters of Recommendation. Using OCR technology and machine learning, this system can scan both printed and digital letters, identifying all critical details such as the name of the candidate, the recommending authority and the key tenets of the recommendation. Once these details are identified, the data extractor converts them into an easily searchable, sortable and digital format, automating the entire process efficiently. The value of a Letter of Recommendation OCR data extractor goes beyond the mere extraction of data. By transforming the unstructured data into a structured format, the technology aids in bringing uniformity. This simplification of the process provides consistent information aiding in better decision-making for reviewers, whether they are in academia, HR departments or any organisation where recommendation letters are integral. The AI-driven approach minimises the effort and manual labour that goes into analysing each letter individually, thus reducing errors and boosting efficiency.
This AI-powered OCR technology can integrate seamlessly into any existing workflows or system, making it a versatile solution. In practical terms, it can significantly cut down the time traditionally spent on manual data entry and review, allowing institutions and businesses to focus on the core areas of their operations. It simplifies the complex process of evaluating recommendation letters, ensuring that important information is recorded accurately, regardless of the volume of letters received. Hence, it is an invaluable asset in time and resource management. A Letter of Recommendation OCR data extractor not only enhances productivity by automating and accelerating the data extraction process but also contributes to more accurate and data-driven decisions. It signifies a notable advancement in handling the critical process involving letters of recommendation, paving the way for smarter, more efficient operations.
Step 1
Upload your documents to Affinda directly to our app, or by integrating with email or document management platforms
Step 2
Step 3
Step 4
Need an AI data extractor customised to your specific business needs? Our expert team can build a model tailored to your business workflows that integrates seamlessly with existing processes.
Personal Details of the Applicant
- Name
- Job Title
- Company Name
Details of the Recommender
- Recommender's Name
- Recommender's Job Title
- Recommender's Company Name
Recommendation Details
- Dates of Applicant's Employment
- Applicant's Responsibilities
- Applicant's Achievements
- Description of Applicant's Skills
- Strengths of the Applicant
Other Details
- Date of the Letter
- Signature of Recommender
Don’t see the fields you need above?
Innovative features that set us apart from the rest.
Superior accuracy
Over 20+ fields extracted
Supports multiple file formats: PDF, JPG, PNG
Highly flexible and customisable
Supports over 56 languages
Upload and extract in bulk
Self-learning models improve over time
Seamless upstream and downstream integration
Admissions teams in educational institutions use our Letter of Recommendation OCR data extractor to streamline the admissions process. The system efficiently collates key information from letters, saving time and improving decision making.
Human Resources (HR) departments benefit from our Letter of Recommendation OCR data extractor during the hiring process. It swiftly pulls data from recommendation letters, providing valuable insights into the candidate's skills and capabilities.
Self-employed professionals such as independent consultants or freelancers can use our Letter of Recommendation OCR to quickly extract and document the key points endorsed in recommendations received, which can be beneficial for their marketing and reputation management.
