Handwriting Recognition of Historical Documents with few labeled data

Historical documents present many challenges for offline handwriting recognition systems, among them, the segmentation and labeling steps. Carefully annotated textlines are needed to train an HTR system. In some scenarios, transcripts are only available at the paragraph level with no text-line information. In this work, we demonstrate how to train an HTR system with few labeled data. Specifically, we train a deep convolutional recurrent neural network (CRNN) system on only 10% of manually labeled text-line data from a dataset and propose an incremental training procedure that covers the rest of the data. Performance is further increased by augmenting the training set with specially crafted multiscale data. We also propose a model-based normalization scheme which considers the variability in the writing scale at the recognition phase. We apply this approach to the publicly available READ dataset. Our system achieved the second best result during the ICDAR2017 competition

Determining the impact of window length on time series forecasting using deep learning

Time series forecasting is a method of predicting the future based on previous observations. It depends on the values of the same variable, but at different time periods. To date, various models have been used in stock market time series forecasting, in particular using deep learning models. However, existing implementations of the models did not determine the suitable number of previous observations, that is the window length. Hence, this study investigates the impact of window length of long short-term memory model in forecasting stock market price. The forecasting is performed on S&P500 daily closing price data set. A different window length of 25-day, 50-day, and 100-day were tested on the same model and data set. The result of the experiment shows that different window length produced different forecasting accuracy. In the employed dataset, it is best to utilize 100 as the window length in forecasting the stock market price. Such a finding indicates the importance of determining the suitable window length for the problem in-hand as there is no One-Size-Fits-All model in time series forecasting

Study of augmentations on historical manuscripts using TrOCR

Historical manuscripts are an essential source of original content. For many reasons, it is hard to recognize these manuscripts as text. This thesis used a state-of-the-art Handwritten Text Recognizer, TrOCR, to recognize a 16th-century manuscript. TrOCR uses a vision transformer to encode the input images and a language transformer to decode them back to text. We showed that carefully preprocessed images and designed augmentations can improve the performance of TrOCR. We suggest an ensemble of augmented models to achieve an even better performance

Handwritten text generation and strikethrough characters augmentation

We introduce two data augmentation techniques, which, used with a Resnet-BiLSTM-CTC network, significantly reduce Word Error Rate and Character Error Rate beyond best-reported results on handwriting text recognition tasks. We apply a novel augmentation that simulates strikethrough text (HandWritten Blots) and a handwritten text generation method based on printed text (StackMix), which proved to be very effective in handwriting text recognition tasks. StackMix uses weakly-supervised framework to get character boundaries. Because these data augmentation techniques are independent of the network used, they could also be applied to enhance the performance of other networks and approaches to handwriting text recognition. Extensive experiments on ten handwritten text datasets show that HandWritten Blots augmentation and StackMix significantly improve the quality of handwriting text recognition models