I N D A . A I

CV Parsing for recruitment

Digitise data, accelerate recruitment processes and save time with CV parsing and information extraction.

Why use CV Parsing and information extraction

Artificial intelligence can help optimise the time required for managing applications and the transfer of large amounts of data, even from the very early recruitment stages. Combined with the work of the recruiter, the Information Extraction and CV Parsing (also Resume Parsing) techniques speed up the process of pulling out relevant candidate information. The steps of compiling the application form and storing data are both automated. As a result, the recruiter can manage applications faster (in digital or paper format), even at Career days, trade fairs or recruiting events. With a quick scan of the CV the digitised information is extracted and added to a database or transferred to other management software. The application process is improved on all fronts: it also improves candidate experience and reduces the dropout rate during the application phase.


Parsing solutions don’t just apply to the HR industry or the search and selection process! Operating in a generalised manner, the parsers automatically recognise and analyse every type of digital document, adapting to the specific internal and sector needs of each company Find out more about Parsing

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What is the information extraction and CV Parsing process

From a textual or image format document (CV), Inda’s Information Extraction and Resume/CV Parsing allow the extraction of a candidate’s unstructured data and their conversion into structured information or documents in XML format. These are automatic processes that can replace the traditional filling in of the application form, accelerating recruiting activities.

  • Recognition of the CV file
  • Text extraction
  • Identification of structured extraction
  • Data mapping

Recognition of the CV file

Attraverso tecniche di Document Layout Analysis (DLA) e Optical Character Recognition (OCR), è possibile distinguere i documenti in formato testuale da quelli in formato immagine. L'analisi del layout, della struttura e delle sezioni permette di differenziare le varie tipologie di CV.
Estensione dei file supportati da Inda: ['pdf', 'doc', 'docx', 'odt', 'txt', 'html', 'pptx', 'rtf', 'jpg', 'jpeg', 'png', 'tif', 'tiff'].

Text axtraction

Dopo aver analizzato il formato del CV, è possibile procedere all'estrazione di un testo ordinato dal documento d'origine. L'operazione di analisi ed esportazione dei dati da un CV è possibile attraverso il CV/Resume Parsing. Ciò consente di ottenere il testo contenuto nel CV e di convertire i dati in esso presenti in informazioni strutturate o documenti in formato XML.

Identification of structured extraction

Attraverso tecniche di Named Entity Recognition (NER) è possibile distinguere entità specifiche come "name", "surname", "job". Grazie alla Relation Extraction (RE), invece, si può comprendere il tipo di relazione che queste entità semantiche hanno con specifiche sezioni del CV.

Tra le entità riconosciute e estratte con Inda​:

Dati personali:
Indirizzo (indirizzo; città; provincia; CAP)
Data di nascita
Livello medio di esperienza (n. esperienze; esperienze totali)

Esperienze professionali
Data inizio
Data fine
Località (città; stato)

Titoli di studio
Data inizio
Data fine
Località (città; stato)
Campo disciplinare

Skills e lingue
Skills (competenza; score)
Lingua (madrelingua; lingua straniera)

Ulteriori informazioni
Lingua CV

Data mapping

Come ultimo step, è necessario mappare il tipo di informazioni estratte in campi predefiniti. In genere i form di candidatura contengono menù a tendina con selezione multipla, per questo è necessario mappare l'informazione estratta nell'opzione più simile tra quelle proposte.

Do you want to know more about Inda's features?

Request a demo and find out how to make the most of the benefits of Information Extraction and CV Parsing to optimise your recruitment

How do information extraction and CV Parsing work

Information Extraction mainly uses technologies integrated in the areas of Computer Vision and Natural Language Processing

Included in Computer Vision technology, this is a process of identifying and analysing the layout and geometric sections of a document.

Integrated with Computer Vision techniques and based on Natural Language Processing algorithms, this is a system that recognises a sequence of characters within an image format in a document.

This is a tool that allows the recognition of the language in which the content of a document (for example a CV) is written.

Starting from the analysis of the format and geometry of the layout (DLA) of a document, this system provides the recognition and extraction of well-ordered text.

Based on Deep Learning algorithms at the heart of Natural Language Processing (NLP), this is an information extraction process through which entity recognition is carried out starting from a text.

Based on Deep Learning and Natural Language Processing (NLP) techniques, this allows relationships between entities recognised by NER to be identified.

The benefits of information extraction and CV Parsing

Improve the Candidate Experience

Increase Return on Investment (ROI)

Increase the Conversion Rate

Enhance Candidate Attraction

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