Methods for CV Conversion to Text Format for Efficient Use with AI

Automation and optimization of recruitment processes are crucial components of company operations. One of the key tasks in recruitment automation is the analysis of resumes, which often have a complex structure, complicating their processing by traditional artificial intelligence methods. This study focuses on the development and evaluation of methods for converting resumes from PDF format to text format for further use in artificial intelligence systems. The paper analyzes the basic problems associated with preserving structural and contextual information during the processing of such documents. The use of the multimodal GPT-4 Vision model for reading and converting text from PDF documents is proposed. A comparison is made between the results obtained using GPT-4 Vision and traditional OCR Tesseract. BLEU, ROUGE-1, and ROUGE-L metrics were used to evaluate accuracy. A total of 157 resumes were collected and analyzed, revealing an increase in discrepancies between texts obtained using GPT-4 Vision and OCR as the volume of text increases.