Character Sequence Prediction Method for Training Data Creation in the Task of Text Recognition

For text line recognition, much attention is paid to augmentation of the training images. Yet the inner structure of the textual information in the images also affects the accuracy of the resulting model. In this paper, we propose an ANN-based method for textual data generation for printing in images with a background of a synthetic training sample. In our method we avoid the usage of completely random sequences as well as the dictionary-based ones. As a result, we gain the data that saves the basic properties of the target language model, such as the balance of vowels and consonants, but avoid the lexicon-based properties, like the prevalence of the specific characters. Moreover, as our method focuses only on high-levels features and does not try to generate the real words, we can use a small training sample and light-weight ANN for text generation. To check our method, we train three ANNs with same architecture, but with different training samples. We choose machine readable zones as a target field because of their structure that does not correspond with the ordinary lexicon. The results of the experiments on three public datasets of identity documents demonstrate the effectiveness of our method and allows to enhance the state-of-the art results for the target field.