Dataset Open Access

ICDAR2013 – Handwritten Digit and Digit String Recognition Competition

Diem, Markus; Fiel, Stefan; Garz, Angelika; Keglevic, Manuel; Kleber, Florian; Sablatnig, Robert

The CVL Single Digit dataset consists of 7000 single digits (700 digits per class) written by approximately 60 different writers. The validation set has the same size but different writers. The validation set may be used for parameter estimation and validation but not for supervised training. The CVL Digit Strings dataset uses 10 different digit strings from a total of about 120 writers resulting in 1262 training images. The digits from the CVL Single Digit dataset were extracted from these strings.

This database may be used for non-commercial research purpose only. If you publish material based on this database, we request you to include a reference to:

Markus Diem, Stefan Fiel, Angelika Garz, Manuel Keglevic, Florian Kleber and Robert Sablatnig, ICDAR 2013 Competition on Handwritten Digit Recognition (HDRC 2013), In Proc. of the 12th Int. Conference on Document Analysis and Recognition (ICDAR) 2013, pp. 1454-1459, 2013.

File Naming The numbers before the first minus are the respective class labels succeeded by an unique ID. 2-0202-21-04.png is an image that contains a single digit with groundtruth 2 135579-0001-10.png is an image that contains the digit string 135579
Files (242.0 MB)
Name Size
cvl-single-digits-completeDatabase.zip
md5:d513d07adb3122db6d5c6fbd9fd4e48b
89.8 MB Download
cvl-single-digits-normalized-completeDatabase.zip
md5:8598dd6f288bad254d6e89d9a26c85d1
19.2 MB Download
cvl-single-digits-train-validation.zip
md5:a2de4dfbaa9b597acb91d19d68c28c3a
34.3 MB Download
cvl-strings-eval.zip
md5:2c6e9379c463116c0a74c419c11f611e
82.9 MB Download
cvl-strings-train.zip
md5:123167109ed2ef554913bd0a9f4d74ba
15.8 MB Download
  • Markus Diem, Stefan Fiel, Angelika Garz, Manuel Keglevic, Florian Kleber and Robert Sablatnig, ICDAR 2013 Competition on Handwritten Digit Recognition (HDRC 2013), In Proc. of the 12th Int. Conference on Document Analysis and Recognition (ICDAR) 2013, pp. 1454-1459, 2013.

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