The usability of synthetic handwritten text to improve machine learning models is assessed for the domain of HTR. Synthetic handwritten text is generated using an existing model based on a GAN. The output of this model is then used to train a state-of-the-art HTR model, which is then applied to recognize real datasets. While this results in a CER of 28.3
This paper presents our motivation, design and two experiments for automatic scoring of handwritten descriptive answers. The first experiment is on scoring of handwritten short descriptive answers in Japanese language exams. We used a deep neural network (DNN)-based handwriting recognizer and a transformer-based automatic scorer without correcting misrecognized characters or adding rubric annotations for scoring. We achieved acceptable agreement between the automatic scoring and the human scoring, while using only 1.7
Old Church Slavonic (OCS) is an ancient language, and it has unique challenges and hurdles in natural language processing. Currently, there is a lack of Python libraries devised for the analysis of OCS texts. This research is not just filling the crucial gap in the computational treatment of OCS language but also producing valuable resources for scholars in historical linguistics, cultural studies, and humanities for the development of further research in the field of ancient language processing. The main contribution of this research work is the development of an algorithm for the lemmatization of OCS texts based on a learned dictionary. The approach can deal with ancient languages without the need for prior linguistic knowledge. Preparing a dataset of more than 330K words of OCS and their corresponding lemmas, this approach integrates the algorithm and dictionary efficiently to achieve accurate lemmatization on test data.
Handwriting analysis in historical documents, crucial for paleography, includes both macroscopic and microscopic examinations. It ranges from assessing the general visual patterns of pages to detailed studies of individual letters. Such analysis not only aids in dating documents and identifying scribes but also provides insights into the evolution of handwriting styles and offers a comprehensive view of historical writing practices. This work proposes a novel end-to-end paleographic system designed for automated handwriting analysis, consisting of three main components: automatic detection and recognition of letters, contrastive hierarchical clustering of the detected letters, and the creation of representative models for the samples in each cluster. These models provide a visual representation of the style variations for each letter. This system processes full manuscript page images to model the diverse shapes of each letter, capturing the inherent variability in historical handwriting styles. The performance of each of the three main system components has been evaluated on a dataset of Greek letters on papyri, achieving results comparable to the state of the art across all three components.
We propose the LD-DOC, a lightweight Document Layout Analysis (DLA) model specifically designed to address the challenge of accurately partitioning document regions under limited data conditions. The LD-DOC model effectively utilizes information from various scale visual features, enhancing its adaptability to feature distributions in scenarios with limited data and thereby improving the accuracy of document region partitioning. Specifically, our model incorporates a feature fusion module comprising a Shallow Feature Enhancement Path (SFEP) and a Cross-Fusion Path (CFP). The SFEP employs a 2D-Discrete Wavelet Transform (2D-DWT) to capture edge features at different scales, which enhances the model's ability to perceive subtle variations and structural information in visual features. This enhancement is crucial for adapting to the nuanced requirements of limited data environments. On the other hand, the CFP uses a Local-Fusion Attention mechanism(LFA) to capture Discrepancy information adaptively among different scales. This approach reduces the model's sensitivity to scale variations and significantly improves its generalization capabilities across diverse document layouts. Furthermore, we introduce the ISCAS-CLAD, a specialized small-scale Chinese Document Layout Analysis Dataset, to demonstrate the effectiveness of our model. Through rigorous testing on ISCAS-CLAD and the PubLayNet datasets, LD-DOC has shown a notable improvement in mean Average Precision (mAP) accuracy, outperforming baseline models by 2.2% and 1.5%, respectively. These results highlight LDDOC's state-of-the-art performance, particularly in challenging datalimited environments, and underscore its potential for practical applications in DLA.
