mlsanityData to 14 Oct 2026

Resume Information Extraction via Post-OCR Text Processing

arXiv 2306.13775 ↗First posted 23 Jun 2023cs.CL cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

Information extraction (IE), one of the main tasks of natural language processing (NLP), has recently increased importance in the use of resumes. In studies on the text to extract information from the CV, sentence classification was generally made using NLP models. In this study, it is aimed to extract information by classifying all of the text groups after pre-processing such as Optical Character Recognition (OCT) and object recognition with the YOLOv8 model of the resumes. The text dataset consists of 286 resumes collected for 5 different (education, experience, talent, personal and language) job descriptions in the IT industry. The dataset created for object recognition consists of 1198 resumes, which were collected from the open-source internet and labeled as sets of text. BERT, BERT-t, DistilBERT, RoBERTa and XLNet were used as models. F1 score variances were used to compare the model results. In addition, the YOLOv8 model has also been reported comparatively in itself. As a result of the comparison, DistilBERT was showed better results despite having a lower number of parameters than other models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 20 references that are in the map. First posted on arXiv 23 Jun 2023.

Builds on

12 of 20 references.

References · 12

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  5. 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,989
  6. 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,475
  7. 2019Cross-lingual Language Model Pretrainingcited by 1,090
  8. 2015An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognitioncited by 520
  9. 2019What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysiscited by 191
  10. 2019Facebook FAIR's WMT19 News Translation Task Submissioncited by 179
  11. 2022A Few-shot Approach to Resume Information Extraction via Promptscited by 4
  12. 2022Construction of English Resume Corpus and Test with Pre-trained Language Modelscited by 2

Built on by

Not cited yet.