Reading CVs is always on the agenda for those who occupy themselves with search and selection. But what happens when a computer reads a CV? While the recruiter can quickly and easily find the information they consider relevant and required for a job posting, it still takes a lot of time to analyse every CV. This is where CV parsing systems can play a fundamental role in the recruiting or talent acquisition process. Let’s find out more.
CV Parsing, also known as Resume Parsing, is a highly beneficial tool for those involved in search and selection. It is a process of extracting and converting unstructured data from a document – in this case, a CV – into structured information, which is then automatically organised and stored within the relevant database. Parsing, automatic extraction and filing of data can be very useful in recruitment activities. A typical parser feature is extracting a CV’s content in “plain text” so that keywords can be searched within the document. The parsing system can also automatically fill out an application form, meaning recruiters can fill out many forms quickly.
Compared to traditional parsing (simple digitisation or text extraction), artificial intelligence has made the process more innovative by focusing on extracting structured information. An ” intelligent” parsing system allows:
Think of the analysis of a CV file, generally arranged in two or more columns. A generic or old-generation parsing software extracts simple, ordered text following the line of the document: since it is text arranged in two columns, the result would be a sentence inconsistent with the original meaning. The meaning of the text can be reconstructed with geometric analysis of the document, but it is of little use in keyword searches (as opposed to what happens with AI parsers). The new parsing system has the advantage of offering a complete understanding of the text. Furthermore, it can read the document, even from a scan or photograph, recognise and read sections and tables, and understand the meaning and relationships between the various parts of the text.
The new parsing systems use specific artificial intelligence (AI) techniques to recognise and extract text and information from a document (CV). To better understand how it works, let’s consider the case of Inda (INtelligent Data Analysis), the AI solution for the HR world, equipped with a parsing system explicitly designed for CV analysis. This system uses computer vision and Natural Language Processing (NLP) techniques to recognise the file structure and the graphic layout, analyse human language, extract text, and map and store valuable data. All to find the most qualified and suitable candidates for the job posting.
To start the parsing process, you begin by uploading a CV file. As we know, many different types of CV layouts and formats exist. Generally, the recruiter reads, analyses and contextualises the information. In the case of parsing, with self-learning AI (machine learning), the system can read and recognise the different formats (over 12 CV file extensions for Inda) by analysing their layout, sections, and structures.
Following the recognition of text and images in the file, information from the CV is analysed and extracted. The personal information, professional experience, education and skills are all removed. This information will be used for self-completion of the application form or the automatic compilation of the candidate’s application. Many companies still ask candidates to fill out lengthy application forms with the risk of losing talent. The intelligent parser tries to avoid this problem by automatically completing application forms.
It is possible that the use of parsers in the recruitment process can raise questions or doubts about the accuracy of the document structure and language recognition. It is true that there are numerous CV formats and that the style of language can vary greatly. For example, how would artificial intelligence distinguish between the name and surname of the candidate and, for example, the company name? Also, would a date be interpreted correctly depending on its format? Ultimately, how is all of the information interpreted? These are legitimate questions, and artificial intelligence systems combat those challenges with techniques (DLA, OCR, NER, etc.) capable of achieving valid and accurate results.
Artificial intelligence applied to the human resources sector can give the recruiter the tools and support necessary to perfect the search and selection process. Artificial intelligence is often chosen to integrate with recruitment software or an Applicant Tracking System (ATS).
This way, some of the candidate’s and the recruiter’s activities are simplified. With CV Parsing, artificial intelligence reads the CV, identifies essential information and automatically completes the application form, making the application experience more positive. In addition, the ability of AI to distinguish images from text, recognise and extract skills and fill in the drop-down menus speeds up the addition of a new application in the system, improving the quality of the recruiter’s work.
The advantages of artificial intelligence are numerous and apply to various stages of the recruitment process. Specifically, CV parsing allows you to:
CV archives are often composed of digital files, saved in folders on company PCs or servers, and CVs are kept in paper format. Saved as such, these documents are not very useful as they do not have any structured information (e.g. name, surname, email, etc.) that can easily be accessed. If all of these CVs were to be processed by a special parser, there would instead be a list of structured information of enormous value. This data could be imported into an Applicant Tracking System (ATS) or help create a database of candidates.
Visit the website for more details on Inda parsing or to learn more about how Inda’s document parsing can be used in other ways.