Avoiding Segmentation in Multi-Digit Numeral String Recognition by Combining Single and Two-Digit Classifiers Trained without Negative Examples

The objective of the present work is to provide an efficient technique for off-line recognition of handwritten numeral strings. It can be used in various applications, like postal code recognition or information extraction from fields of different forms. The proposed solution uses convolutional neural networks (CNNs) to implement two classifiers, one for digit recognition and one for numeral strings composed from two digits partially overlapped. Both classifiers are trained without negative examples. By comparing the results of the classifiers it can decide if the image contains one digit or two partially overlapped digits. The use of the two-digit strings classifier completely relieves our method from the usage of segmentation. The method is evaluated on a well-known numeral strings database - NIST Special Database 19 - and the results are comparable with the best results from literature, even if those are using elaborate segmentation and training with negative examples.