R-GNN: recurrent graph neural networks for font classification of oracle bone inscriptions

The Dongba manuscripts are a unique primitive pictographic writing system that originated among the Naxi people of Lijiang, China, boasting over a thousand years of history. The uniqueness of the Dongba manuscripts stems from their pronounced pictorial and ideographic characteristics. However, the digital preservation and inheritance of Dongba manu manuscripts face multiple challenges, including extracting its rich semantic information, recognizing individual characters, retrieving Dongba manuscripts, and automatically interpreting the meanings of Dongba manuscripts. Developing efficient Dongba character detection technology has become a key research focus, wherein establishing a standardized Dongba detection dataset is crucial for training and evaluating techniques. In this study, we have created a comprehensive Dongba manuscripts detection dataset covering various commonly used Dongba characters and vocabularies. Additionally, we propose a model named STEF. Firstly, the Swin Transformer extracts the complex structures and diverse shapes of Dongba manuscripts’ features. Then, by introducing a Feature Pyramid Enhancement Module, features of different sizes are cascaded to preserve multi-scale information. Subsequently, all features are fused in a FUSION module, resulting in features of various Dongba manuscript styles. Each pixel’s binarisation threshold is dynamically adjusted through a differentiable binarisation operation, accurately distinguishing between foreground Dongba manuscripts and background. Lastly, deformable convolution is introduced, allowing the model to dynamically adjust the convolution kernel’s size and shape based on the Dongba manuscripts’ size, thereby better capturing the detailed information of Dongba characters of different sizes. Experimental results show that STEF achieves a recall rate of 88.88%, a precision rate of 88.65%, and an F-measure of 88.76%, outperforming other text detection algorithms. Visualization experiments demonstrate that STEF performs well in detecting Dongba manuscripts of various sizes, shapes, and styles, especially in blurred handwriting and complex backgrounds.

STEF: a Swin Transformer-Based Enhanced Feature Pyramid Fusion Model for Dongba character detection

Ma,

Chen,

et al. 2024

Integrating path signature and pen-tip trajectory features for online handwriting Yi text recognition

Xiao,

Chen,

et al. 2024

Recognizing online handwriting Yi text is crucial for recording and preserving Yi literature. However, the scarcity of online handwriting Yi text datasets has limited relevant research, impeding the process of Yi informatization. In this work, we use synthetic data to train models, and an Online Handwriting Yi Text Recognition Network (YTRN) is proposed, which extracts robust character features to address the gap between synthetic and real data. YTRN adeptly learns the spatial structure features from path signature feature maps and captures trajectory features from the pen-tip trajectories. Subsequently, an innovative adaptive feature fusion module integrates these two sets of features to yield more comprehensive and robust character representations. Experiments on our newly collected Yi-OLHWDB2.0 dataset demonstrate that our method outperforms previous approaches, achieving an impressive 95.67% accuracy. This highlights the model’s effectiveness in extracting comprehensive and robust features from path signature maps and pen-tip trajectories, significantly enhancing recognition accuracy and generalization.

Ancient Tamil inscription recognition using detect, recognize and labelling, interpreter framework of text method

Murugan,

Visalakshi

2024

Tamil is the oldest language spoken in Tamil Nadu, India, with inscriptions dating back to the third century BCE found in caves, temples, and archaeological sites. The style and content of these inscriptions have evolved over time, reflecting changes in society, governance, and language usage. They provide valuable insights into rulers, dynasties, administrative systems, religious practices, and societal norms of their era. However, the diverse fonts and styles of these inscriptions necessitate an efficient method for alphabet and word recognition. Existing algorithms primarily recognize Tamil words and characters from the nineteenth century and do not address the language and styles used in the third century. This study proposes a novel DR-LIFT framework specifically designed for recognizing Tamil inscriptions from this earlier period, overcoming the limitations of current methods. The dataset used consists of third-century Tamil inscriptions. The algorithms within the DR-LIFT method specifically designed to detect text with intricate features such as curves, loops, and lines, significantly enhancing detection accuracy. The proposed framework achieves impressive outcomes, with a recognition accuracy of 99% and a recognition rate of 98.8%.