Hello trend,
This is Satya Mallick from LearnOpenCV.com.
We have a new post for our OCR Nerds!
Optical Character Recognition (OCR) has seen several innovations over the years. Despite a long history and several state-of-the-art models, researchers continued to innovate. Like many other fields in deep learning, OCR also saw the importance and impact of transformer neural networks.
Today, we have models like TrOCR (Transformer OCR) which truly surpass the previous techniques in terms of accuracy. We will explore the new state-of-the-art model in today's post where you'll learn:
✅ What is the architecture of TrOCR?
✅ What models does the TrOCR family include?
✅ How were the TrOCR models pretrained?
✅ How to run inference using TrOCR and Hugging Face?
So without further ado, let's jump into the tutorial
| TrOCR – Getting Started with Transformer Based OCR |
Accompanying code for the blog post can be found here:
| Download Code |
ChatGPT Live Class
In case you missed it, I am conducting a free technical webinar where you will learn how to build an application using ChatGPT for analyzing and matching thousands of resumes to a job description.
⌛ When? Sep 17 (Sunday) 9 AM to 12 PM Pacific Time
Register using the link below. Seats are limited.
| Register Now |
The following topics will be covered in depth.
✔️ Enhanced Text Extraction: Implement techniques for extracting text from various document formats, such as PDF, DOC, and DOCX files.
✔️ Intelligent Structuring of Unstructured Data: Utilize GPT API to transform unstructured text into structured data, facilitating the extraction of key information such as name, email, and phone number from resumes. Just a few months back, you had to be an expert in Natural Language Processing to pull this off.
✔️ Automated Resume Summarization: Develop an automated system for summarizing resumes, highlighting the most pertinent information for recruiters and HR professionals, thereby speeding up the initial screening process.
✔️ ChatGPT for Information Extraction: Learn custom prompts to efficiently extract specific information from resumes, such as technical skills and years of experience, making the recruitment process more efficient.
✔️ Job Description Requirement Extraction: Implement prompts and GPT API to automatically extract essential requirements and qualifications from job descriptions, aiding in the creation of a structured checklist of criteria for candidate evaluation.
✔️ Resume and Job Description Matching: Develop a smart matching system that compares the structured data of a resume with the requirements extracted from a job description. This will provide insights into any missing elements and enable the system to rate a resume against the job requirements, assisting recruiters in shortlisting candidates more effectively.
If that sounds interesting, please use the link below to register.
| Register Now |
Cheers,
Satya
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