- Nos Solutions
- Features
- Our use cases
- Nos cas d'usage
- pdf - Retrieve 100 information from a report
- pdf - Aggregate data from diverse documents
- pdf - Retrieve information from a paper directory
- pdf - Retrieve invoice data
- pdf - Label documents
- web - Building content from an EAN code
- web - Aggregate informations from multiple websites
- web - Create an argus from web data
- mail - Create a pre-ticket from an email
- CRM - Clean data upstream of a CRM
- Prices
- About
- Contact
Document labeling
Context
How to label documents of various types and determine their usability?
A bank specializing in automobile financing for individuals needs to improve its process for processing loan applications. Each reseller sends the financing request documents in files without description. Part of the recognition of documents is done by an offshore service and knowing if the files are complete takes 3 to 4 days.
The mission consists of a double labeling of documents. On the one hand, knowing what type is a document among the 25 possible types; on the other hand, correctly label the usable and unusable documents, in order in the latter case, to immediately send a request for confirmation to the reseller.lallowing him to return the file .
Example of an exploitable document
Example of non-exploitable document
Labellisation de documents
Contexte
Comment labelliser des documents de types variés et déterminer leur exploitabilité ?
Une banque spécialiste du financement automobile des particuliers a besoin d'améliorer son processus de traitement des demandes de crédits. Chaque revendeur envoie les documents des demandes de financement dans des dossiers sans descriptif.
La mission consiste en une double labellisation des documents. D'une part, savoir de quel type est un document parmi les 25 types possibles; d'autre part, labelliser correctement les documents exploitables et inexploitables.
Exemple de document exploitable |
Exemple de document non exploitable |
Scénario utilisé
Worker OCR
Récupère le texte à partir d'une image numérisée
WorkerETL
Nettoie et met en forme les données
Worker
Détermine l'exploitabilité des documents
WorkerDatabase
Envoie les fichiers vers le service validation
Return on investment
Cleaning, standardization, control and consolidation of information. | Double labellisation automatique des documents | 3 jours de gagnés sur la demande de financement |
Parlons ensemble de votre projet !
Scénario utilisé
1
Worker OCR
Recovers text from a scanned image
2
WorkerETL
Cleanses and formats data
3
Worker
Add the INSEE codes to the municipalities
4
WorkerDatabase
Save to database
Let's talk about your project together! Let's talk about your project together!
Cleaning, standardization, control and consolidation of information.
Automatic double labeling of documents
3 days saved on the financing request