Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching

This article reports on an ongoing project aiming at automatization of pseudonymization of learner essays.The process includes three steps: identification of personal information in an unstructured text, labeling for a category, and pseudonymization.We experiment with rule-based methods for detection of 15 categories out of the suggested 19 ( Megyesi et al., 2018) that we deem important and/or doable with automatic approaches.For the detection and labeling steps, we use resources covering personal names, geographic names, company and university names and others.For the pseudonymization step, we replace the item using another item of the same type from the above-mentioned resources.Evaluation of the detection and labeling steps are made on a set of manually anonymized essays.The results are promising and show that 89% of the personal information can be successfully identified in learner data, and annotated correctly with an inter-annotator agreement of 86% measured as Fleiss kappa and Krippendorff's alpha.

European libraries and archives are filled with enciphered manuscripts from the early modern period. These include military and diplomatic correspondence, records of secret societies, private letters, and so on. Although they are enciphered with classical cryptographic algorithms, their contents are unavailable to working historians. We therefore attack the problem of automatically converting cipher manuscript images into plaintext. We develop unsupervised models for character segmentation, character-image clustering, and decipherment of cluster sequences. We experiment with both pipelined and joint models, and we give empirical results for multiple ciphers.

This paper presents a study on if and how automatically extracted keywords can be used to improve text categorization. In summary we show that a higher performance --- as measured by micro-averaged F-measure on a standard text categorization collection --- is achieved when the full-text representation is combined with the automatically extracted keywords. The combination is obtained by giving higher weights to words in the full-texts that are also extracted as keywords. We also present results for experiments in which the keywords are the only input to the categorizer, either represented as unigrams or intact. Of these two experiments, the unigrams have the best performance, although neither performs as well as headlines only.

We investigate the relationship between prosodic phrase boundaries in terms of pausing and the linguistic structure on morphosyntactic and discourse levels in spontaneous dialogues as well as in read aloud speech in Swedish. Both the speakers' production and the listeners' perception of pausing are considered and mapped to the linguistic structure.

This study aims to investigate the length, frequency and position of various types of pauses in three different speaking styles: elicited spontaneous dialogues, professional reading and nonprofessional reading.