A Generic OCR Using Deep Siamese Convolution Neural Networks

This paper presents a generic optical character recognition (OCR) system based on deep Siamese convolution neural networks (CNNs) and support vector machines (SVM). Supervised deep CNNs achieve high level of accuracy in classification tasks. However, fine-tuning a trained model for a new set of classes requires large amount of data to overcome the problem of dataset bias. The classification accuracy of deep neural networks (DNNs) degrades when the available dataset is insufficient. Moreover, using a trained deep neural network in classifying a new class requires tuning the network architecture and retraining the model. All these limitations are handled by our proposed system. The deep Siamese CNN is trained for extracting discriminative features. The training is performed once using a group of classes. The OCR system is then used for recognizing different classes without retraining or fine-tuning the deep Siamese CNN model. Only few samples are needed from any target class for classification. The proposed OCR system is evaluated on different domains: Arabic letters, Eastern-Arabic numerals, Hindu-Arabic numerals, and Farsi numerals using test sets that contain printed and handwritten letters and numerals. The proposed system achieves a very promising recognition accuracy close to the results achieved by CNNs trained for specific target classes and recognition systems without the need for retraining. The system outperforms the state of the art method that uses Siamese CNN in one-shot classification task by around 12%.