Off-line recognition of handwritten numeral strings composed from two-digits partially overlapped using Convolutional Neural Networks

The objective of the present work is to provide an efficient and reliable technique for off-line recognition of handwritten numerals composed from two digits partially overlapped. It can be used in various applications, like postal code recognition or information extraction from fields of different forms. Proposed solution uses convolutional neural networks (CNNs) and rely on very light preprocessing avoiding segmentation. Test results on a comprehensive well-known character database -NIST SD 19- show a high degree of recognition accuracy.