The Image to Text project is an innovative application designed to streamline the process of extracting information from resumes. By leveraging optical character recognition (OCR) technology, this project aims to automate the tedious task of manually entering data from resumes into digital formats.
Using this application is simple and efficient. Users can capture an image of a resume using their mobile device or upload a scanned document. The project's advanced image processing algorithms then analyze the image and extract the textual content from it. The extracted text is then processed and organized into relevant details such as name, contact information, education, work experience, and skills.
With the Image to Text project, you can save significant time and effort that would otherwise be spent manually transcribing resume information. Whether you are a recruiter, HR professional, or individual job seeker, this application proves to be a valuable tool for enhancing productivity and efficiency in the hiring process.
The Image to Text project offers a user-friendly interface and robust functionality, making it an invaluable asset in today's fast-paced and digitally-driven recruitment landscape. Simplify your resume processing tasks and unlock the power of automation with this powerful application.
| Name | Branch | Registration No. |
|---|---|---|
| Utkarsh Jha | Computer Science and Engineering | 20214138 |
| Urbi Das | Mechanical Engineering | 20213057 |
| Name | Branch | Registration No. |
|---|---|---|
| Anurag Gupta | Electronics and Communication Engineering | 20195168 |
| Shashank Singh | Electrical Engineering | 20202085 |
- Python
- OpenCV
- Tesseract OCR
- HTML/CSS
- Git (version control)
- Care has been taken to make the application as user friendly as possible with an appealing GUI.
- A file upload button has been provided for the user to upload the image of a resume.
- Then the application, using the OCR engine and some preprocessing steps, extracts the important details like the phone number, email, address, etc. of the candidate and displays it as its output.
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Automated Resume Processing: An OCR-based resume extractor automates the process of extracting information from resumes, eliminating the need for manual data entry. It saves time and effort, especially for recruiters or HR professionals handling a large volume of resumes.
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Efficient Candidate Screening: The resume extractor enables quick and efficient candidate screening by extracting key details such as name, contact information, education, work experience, and skills. This helps recruiters to easily review and compare relevant information across multiple resumes.
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Improved Accuracy and Consistency: By utilizing OCR technology, the resume extractor ensures high accuracy and consistency in extracting text from resumes. It reduces the chances of human error that may occur during manual data entry.
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Enhanced Keyword Search and Filtering: The extracted information can be used to perform keyword searches and filtering based on specific criteria or qualifications. This allows recruiters to quickly identify resumes that meet specific requirements and narrow down the candidate pool.
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Integration with Applicant Tracking Systems (ATS): The extracted resume data can be seamlessly integrated into ATS or HR software systems, enabling streamlined resume management and improved data organization. It facilitates better collaboration among team members and simplifies the hiring process.
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Customizable Data Extraction: An OCR-based resume extractor can be customized to extract additional information specific to the organization's requirements. This includes extracting data from custom sections or templates within the resume.
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Scalability and Time Savings: With the ability to process resumes in a matter of seconds, the OCR-based resume extractor enables scalability, allowing recruiters to handle a large number of resumes efficiently. It saves considerable time compared to manual processing, resulting in faster candidate evaluation and hiring decisions.
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Improved Candidate Experience: By automating the resume processing stage, the resume extractor reduces the waiting time for candidates, providing a more seamless and responsive hiring experience.
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Improved Accuracy: Continuously enhance the OCR engine to achieve higher accuracy through research and development efforts.
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Advanced Data Extraction: Expand the capabilities to extract additional information like skills, certifications, and project details.
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Natural Language Processing (NLP) Integration: Integrate NLP techniques for deeper analysis and understanding of resume content.
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Semantic Understanding: Develop the ability to comprehend the contextual meaning of resume content and extract structured information accordingly.
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Resume Parsing: Implement advanced parsing techniques to handle resumes in various formats, such as PDF, Word documents, and scanned images.
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Integration with HR Systems: Establish seamless integration with popular HR systems like ATS or HRIS for direct data synchronization.
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User Interface Enhancement: Improve the user interface with a user-friendly dashboard and intuitive features for managing extracted resume data.
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Data Privacy and Security: Ensure compliance with data privacy regulations and implement robust security measures.
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Machine Learning for Candidate Ranking: Utilize machine learning algorithms to develop candidate ranking models based on extracted resume data.
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Feedback and Iterative Improvements: Gather user feedback to continually refine and improve the performance of the resume extractor.