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This paper introduces a new offline handwriting database that was developed to be employed in performance evaluation, result comparison and development of new methods related to handwriting analysis and recognition. The database can particularly be used for signature verification, writer recognition and writer demographics classification. In addition, the database also supports isolated digit recognition, digit/text segmentation and recognition and similar related tasks. The database comprises 600 Arabic and 600 French text samples, 1300 signatures and 21,000 digits. 100 Algerian individuals coming from different age groups and educational backgrounds contributed to the development of database by providing a total of 1300 forms. The database is also accompanied with ground truth data supporting the evaluation of the aforementioned tasks. The main contribution of the database is providing a multi-script platform where same authors contributed samples in French and Arabic. It would be interesting to explore applications like writer recognition and writer demographics classification in a multiscript environment.

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We propose in this paper a system to recognize handwritten digit strings, which constitutes a di±cult task because of overlapping and/or joining of adjacent digits. To resolve this problem, we use a segmentation-veri¯cation of handwritten connected digits based conjointly on the oriented sliding window and support vector machine (SVM) classi¯ers. The proposed approach allows separating adjacent digits according the connection con¯guration by¯nding at the same time the interconnection points between adjacent digits and the cutting path. SVM-based segmentation-veri¯cation using the global decision module allows the rejection or acceptance of the processed image. Experimental results conducted on a large synthetic database of handwritten digits show the e®ective use of the oriented sliding window for segmentationveri¯cation.