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BALTHAZAR
SIMON
BENES
LA LOVE
DAPHNE
LUCIE
NASSIM
ASSRAOUI
LAVIAN
MAEVA
EMMA
MOULINIER
ELISE
HONNERT
MATHEO
PETITDIDIER
PAULINE
LOUVENAZ
BOURQUIN
ROMAIN
ASMA
CYRIELLE
LILOU
ESTEBANN
MITHIEUX
MARION
THOMAS
ANAIS
BROLL
JAFFEUX
ANNE
PREVOST
ROMANE
BRUGERIE
NOLAN
LORENTIN
ELISA
PAULINE
FRANCOIS
MAUPAS
MEISSA
REBACH
ERWAN
AMBROISE
LAURA
AHMED-KHODJA
LOISE
ELBAKKALI
BENZINA
LAQUERRIERE
YAEL
VITRE
GUILLOT GOGUET
BOLOZAN
MATHEO
SHA I
VICTORIA
JULIE
BARBIER
GILLES-LAWRENCE
DUPRAT
LABARH
REMI
BLANLO
ARGITXU
SINEM
LISON
PAYEN-MERLE
INES
NAWFEL
WADSWORRE
CROCHARD
FREDERIC
RODRIGUES
AUBANE
ELISA
ACHOURI
MAEVA
GRINAND
ANTOINE
SANA
ENZO
DOMAS
MALOLEPSZY
THOMAS
JULIE
KADIR EREN
PERIOL
BGUGEAU
SOCHET
TROUVAT
GARGUEB
CORALIE
VANDENABEELE
TEZKRAT
ASHLEY
CAGNARD
SANOGO
AMBROSS
GAROT

The dataset comprises over four hundred thousand handwritten names obtained from charitable initiatives. Character Recognition employs image processing techniques to transform characters present on scanned documents into digital formats. It generally exhibits good performance with machine-printed fonts. Nonetheless, machines still encounter formidable obstacles in accurately identifying handwritten characters due to the vast diversity in individual writing styles.

The total number of first names was 206,799, while the total number of surnames was 207,024. The data was partitioned into a training set (330,396 samples), testing set (41,300 samples), and validation set (41,292 samples) respectively.

The DATASET has been cleaned from empty or partly empty entries.

I am not the owner of this dataset. I took this dataset from kaggle and transformed it to a huggingface dataset to make it easier to work with.

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