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Automatic recognition of handwritten digit string with unknown length has many potential real applications. The most challenging step in this problem is how to efficiently segment connected and/or overlapped digits exhibited in the input image. Most existing numeral string segmentation approaches combine several segmentation hypotheses to handle various types of connected digits. This paper proposes a new handwritten digit string recognition without applying any explicit segmentation techniques. The proposed method uses a new cascade of hybrid principal component analysis network (PCANet) and support vector machine (SVM) classifier called PCA-SVMNet. PCANet is an emerging unsupervised simple deep neural network typically with only two convolutional layers. The proposed PCA-SVMNet model adds a new fully connected layer trained separately using SVM optimization method. Cascaded stages of …