Chaque \u00e9quipe financi\u00e8re a pass\u00e9 du temps \u00e0 taper des chiffres provenant de re\u00e7us papier dans une feuille de calcul ou un syst\u00e8me comptable. C'est lent, sujet aux erreurs et totalement inutile en 2024. La reconnaissance optique de caract\u00e8res (ROC), ou OCR en anglais, transforme cette t\u00e2che manuelle en une t\u00e2che automatis\u00e9e. En lisant une photo ou une num\u00e9risation d'un re\u00e7u et en convertissant le texte imprim\u00e9 en donn\u00e9es structur\u00e9es, l'OCR \u00e9limine la saisie r\u00e9p\u00e9titive \u00e0 la source. Pour les entreprises qui traitent des dizaines ou des centaines de re\u00e7us par mois, les gains de temps sont consid\u00e9rables. Cet article explique ce qu'est l'OCR pour les re\u00e7us, comment fonctionne la technologie sous-jacente, pourquoi elle est importante pour les \u00e9quipes financi\u00e8res et comment Docnova la met en pratique.<\/p>\n\n\n\n
OCR signifie Reconnaissance Optique de Caract\u00e8res. C'est une technologie qui analyse une image, qu'il s'agisse d'un document num\u00e9ris\u00e9, d'une photo prise avec un t\u00e9l\u00e9phone ou d'un PDF, et identifie les caract\u00e8res qu'elle contient, les convertissant en texte lisible par machine.<\/p>\n\n\n\n
Dans le contexte des re\u00e7us, l'OCR va plus loin. Une image brute d'un re\u00e7u contient du texte non structur\u00e9 : le nom d'un commer\u00e7ant, une date, des articles, un sous-total, un montant de TVA et un total g\u00e9n\u00e9ral sont tous pr\u00e9sent\u00e9s dans le format que le syst\u00e8me de point de vente a choisi. L'OCR moderne sur les re\u00e7us n'extrait pas seulement les caract\u00e8res, il les analyse et les associe \u00e0 des champs nomm\u00e9s : commer\u00e7ant, date, devise, montant net, montant de la TVA, total. Cette sortie structur\u00e9e est ce qui rend les donn\u00e9es imm\u00e9diatement utilisables dans les logiciels de finance sans travail manuel suppl\u00e9mentaire.<\/p>\n\n\n\n
Les premiers syst\u00e8mes de reconnaissance optique de caract\u00e8res (ROC) n\u00e9cessitaient un texte dactylographi\u00e9 propre sur du papier blanc. Les syst\u00e8mes actuels utilisent des mod\u00e8les d'apprentissage automatique entra\u00een\u00e9s sur des millions de mises en page de documents, ce qui les rend robustes aux notes manuscrites, aux photos inclin\u00e9es, aux arri\u00e8re-plans color\u00e9s et aux formats de re\u00e7us vari\u00e9s provenant de diff\u00e9rents pays.<\/p>\n\n\n\n
Le processus se d\u00e9roule en plusieurs \u00e9tapes. D'abord, l'image est pr\u00e9trait\u00e9e : la rotation est corrig\u00e9e, le contraste est am\u00e9lior\u00e9 et le bruit est r\u00e9duit pour donner au moteur de reconnaissance une entr\u00e9e aussi nette que possible.<\/p>\n\n\n\n
Deuxi\u00e8mement, le moteur d\u00e9tecte des r\u00e9gions de texte sur la page - blocs, lignes et caract\u00e8res individuels - et les lit. Cela produit une cha\u00eene de texte brute qui refl\u00e8te ce qui est imprim\u00e9 sur le re\u00e7u.<\/p>\n\n\n\n
Troisi\u00e8mement, une couche d'analyse interpr\u00e8te le texte brut. Elle utilise la reconnaissance de formes et des r\u00e8gles contextuelles pour identifier quelle cha\u00eene de caract\u00e8res correspond au nom du commer\u00e7ant, quelle est la date, quels chiffres correspondent aux prix des articles par rapport au total, et quelle valeur repr\u00e9sente la TVA. Sur les re\u00e7us, cette \u00e9tape est particuli\u00e8rement importante car il n'y a pas de sch\u00e9ma fixe, chaque commer\u00e7ant pr\u00e9sentant ses re\u00e7us diff\u00e9remment.<\/p>\n\n\n\n
Enfin, le r\u00e9sultat structur\u00e9 est valid\u00e9 : les symboles mon\u00e9taires sont normalis\u00e9s, les formats de date sont standardis\u00e9s et les montants sont v\u00e9rifi\u00e9s pour leur coh\u00e9rence interne (les postes individuels s'additionnent-ils au sous-total ?). Les donn\u00e9es valid\u00e9es sont ensuite \u00e9crites dans le relev\u00e9 de d\u00e9penses.<\/p>\n\n\n\n
Le b\u00e9n\u00e9fice le plus imm\u00e9diat est la rapidit\u00e9. Une facture qui prendrait deux \u00e0 trois minutes \u00e0 saisir manuellement est trait\u00e9e en quelques secondes. Sur un mois de d\u00e9penses d'employ\u00e9s, cela se traduit par des heures r\u00e9cup\u00e9r\u00e9es.<\/p>\n\n\n\n
La pr\u00e9cision s'am\u00e9liore \u00e9galement. La saisie manuelle des donn\u00e9es introduit des erreurs de transcription \u2014 chiffres invers\u00e9s, mauvaises dates, d\u00e9cimales oubli\u00e9es. L'OCR supprime l'\u00e9tape de copie humaine, de sorte que les donn\u00e9es de votre syst\u00e8me correspondent \u00e0 celles de la facture.<\/p>\n\n\n\n
Il existe \u00e9galement des avantages en mati\u00e8re de conformit\u00e9 et d'audit. Lorsque chaque re\u00e7u est captur\u00e9 num\u00e9riquement avec son image d'origine jointe, le relev\u00e9 de d\u00e9penses est complet et tra\u00e7able. Les auditeurs peuvent v\u00e9rifier le document original \u00e0 tout moment. Les responsables financiers peuvent filtrer, rechercher et g\u00e9n\u00e9rer des rapports sur les d\u00e9penses par commer\u00e7ant, cat\u00e9gorie, p\u00e9riode de date ou montant de TVA, ce qui n'est pas pratique avec un dossier de re\u00e7us papier.<\/p>\n\n\n\n
Enfin, la reconnaissance optique de caract\u00e8res (OCR) permet l'application des politiques. Une fois que les donn\u00e9es de d\u00e9penses sont structur\u00e9es, elles peuvent \u00eatre v\u00e9rifi\u00e9es automatiquement par rapport \u00e0 des r\u00e8gles : limites de d\u00e9penses par cat\u00e9gorie, listes de marchands approuv\u00e9s ou d\u00e9tection de soumissions en double.<\/p>\n\n\n\n
Dans Docnova, la page de suivi des re\u00e7us et des d\u00e9penses prend en charge deux m\u00e9thodes de cr\u00e9ation de d\u00e9penses : la saisie manuelle et le t\u00e9l\u00e9chargement de documents. L'option \u201cT\u00e9l\u00e9charger un document\u201d accepte une image ou un PDF de re\u00e7u et d\u00e9clenche automatiquement l'extraction OCR.<\/p>\n\n\n\n
Apr\u00e8s le t\u00e9l\u00e9chargement, Docnova extrait les champs cl\u00e9s et remplit un enregistrement de d\u00e9pense avec le nom du commer\u00e7ant, la date de d\u00e9pense, la devise, le montant de la TVA et le montant TTC, les m\u00eames colonnes visibles dans la liste des d\u00e9penses. La source d'OCR utilis\u00e9e pour l'extraction est configurable dans les param\u00e8tres de l'IA, permettant aux \u00e9quipes d'ajuster le moteur \u00e0 leurs types de documents.<\/p>\n\n\n\n
Les d\u00e9penses captur\u00e9es de cette mani\u00e8re alimentent directement le reporting financier. L'indicateur cl\u00e9 de performance (ICP) \"D\u00e9penses totales des re\u00e7us\" sur la page \"Aper\u00e7u financier\" agr\u00e8ge tous les re\u00e7us t\u00e9l\u00e9charg\u00e9s et saisis manuellement, offrant aux \u00e9quipes financi\u00e8res une vue en temps r\u00e9el des d\u00e9penses hors facture aux c\u00f4t\u00e9s de leurs chiffres de revenus et de d\u00e9penses par facture.<\/p>\n\n\n\n
La reconnaissance optique de caract\u00e8res (OCR) pour les re\u00e7us \u00e9limine le travail manuel de saisie des d\u00e9penses, am\u00e9liore l'exactitude des donn\u00e9es et cr\u00e9e une piste d'audit num\u00e9rique compl\u00e8te. Pour les \u00e9quipes financi\u00e8res qui g\u00e8rent les d\u00e9penses des employ\u00e9s, les re\u00e7us des fournisseurs ou la petite caisse, il s'agit de l'une des automatisations les plus efficaces disponibles.<\/p>","protected":false},"excerpt":{"rendered":"
Every finance team has spent time typing numbers from paper receipts into a spreadsheet or accounting system. It is slow, error-prone, and completely unnecessary in 2024. OCR Optical Character Recognition turns that manual task into an automated one. By reading a photo or scan of a receipt and converting the printed text into structured data, 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