Obtaining the original, clean forms of struck-through handwritten words can be of interest to literary scholars, focusing on tasks such as genetic criticism. In addition to this, replacing struck-through words can also have a positive impact on text recognition tasks. This work presents a novel unsupervised approach for strikethrough removal from handwritten words, employing cycle-consistent generative adversarial networks (CycleGANs). The removal performance is improved upon by extending the network with an attribute-guided approach. Furthermore, two new datasets, a synthetic multi-writer set, based on the IAM database, and a genuine single-writer dataset, are introduced for the training and evaluation of the models. The experimental results demonstrate the efficacy of the proposed method, where the examined attribute-guided models achieve F1 scores above 0.8 on the synthetic test set, improving upon the performance of the regular CycleGAN. Despite being trained exclusively on the synthetic dataset, the examined models even produce convincing cleaned images for genuine struck-through words. 

Transcribing struck-through, handwritten words, for example for the purpose of genetic criticism, can pose a challenge to both humans and machines, due to the obstructive properties of the superimposed strokes. This paper investigates the use of paired image to image translation approaches to remove strikethrough strokes from handwritten words. Four different neural network architectures are examined, ranging from a few simple convolutional layers to deeper ones, employing Dense blocks. Experimental results, obtained from one synthetic and one genuine paired strikethrough dataset, confirm that the proposed paired models outperform the CycleGAN-based state of the art, while using less than a sixth of the trainable parameters.

One of the factors limiting the performance of handwritten text recognition (HTR) for stenography is the small amount of annotated training data. To alleviate the problem of data scarcity, modern HTR methods often employ data augmentation. However, due to specifics of the stenographic script, such settings may not be directly applicable for stenography recognition. In this work, we study 22 classical augmentation techniques, most of which are commonly used for HTR of other scripts, such as Latin handwriting. Through extensive experiments, we identify a group of augmentations, including for example contained ranges of random rotation, shifts and scaling, that are beneficial to the use case of stenography recognition. Furthermore, a number of augmentation approaches, leading to a decrease in recognition performance, are identified. Our results are supported by statistical hypothesis testing. A link to the source code is provided in the paper.

In this paper, we establish the first baseline for handwritten stenography recognition, using the novel LION dataset, and investigate the impact of including selected aspects of stenographic theory into the recognition process. We make the LION dataset publicly available with the aim of encouraging future research in handwritten stenography recognition. A state-of-the-art text recognition model is trained to establish a baseline. Stenographic domain knowledge is integrated by transforming the target sequences into representations which approximate diplomatic transcriptions, wherein each symbol in the script is represented by its own character in the transliteration, as opposed to corresponding combinations of characters from the Swedish alphabet. Four such encoding schemes are evaluated and results are further improved by integrating a pre-training scheme, based on synthetic data. The baseline model achieves an average test character error rate (CER) of 29.81% and a word error rate (WER) of 55.14%. Test error rates are reduced significantly (p< 0.01) by combining stenography-specific target sequence encodings with pre-training and fine-tuning, yielding CERs in the range of 24.5–26% and WERs of 44.8–48.2%. An analysis of selected recognition errors illustrates the challenges that the stenographic writing system poses to text recognition. This work establishes the first baseline for handwritten stenography recognition. Our proposed combination of integrating stenography-specific knowledge, in conjunction with pre-training and fine-tuning on synthetic data, yields considerable improvements. Together with our precursor study on the subject, this is the first work to apply modern handwritten text recognition to stenography. The dataset and our code are publicly available via Zenodo.