Braille Character Segmentation Algorithm Based on Gaussian Diffusion

Optical braille recognition methods typically employ existing target detection models or segmentation modelsfor the direct detection and recognition of braille characters in original braille images. However, these methodsneed improvement in accuracy and generalizability, especially in densely dotted braille image environments. Thispaper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithmbased on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. Thisis applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining thecentral coordinates of the braille convex dots. The second stage involves constructing a braille grid using traditionalpost-processing algorithms to recognize braille character information. Experimental results demonstrate that thisframework exhibits strong robustness and effectiveness in detecting braille dots and recognizing braille charactersin complex double-sided braille image datasets. The framework achieved an F1 score of 99.89% for Braille dotdetection and 99.78% for Braille character recognition. Compared to the highest accuracy in existing methods,these represent improvements of 0.08% and 0.02%, respectively.

Comprehensive Learning Strategy Enhanced Chaotic Whale Optimization for High-dimensional Feature Selection

Feature selection(FS)is an adequate data pre-processing method that reduces the dimensionality of datasets and is used in bioinformatics,finance,and medicine.Traditional FS approaches,however,frequently struggle to identify the most important characteristics when dealing with high-dimensional information.To alleviate the imbalance of explore search ability and exploit search ability of the Whale Optimization Algorithm(WOA),we propose an enhanced WOA,namely SCLWOA,that incorporates sine chaos and comprehensive learning(CL)strategies.Among them,the CL mechanism contributes to improving the ability to explore.At the same time,the sine chaos is used to enhance the exploitation capacity and help the optimizer to gain a better initial solution.The hybrid performance of SCLWOA was evaluated comprehensively on IEEE CEC2017 test functions,including its qualitative analysis and comparisons with other optimizers.The results demonstrate that SCLWOA is superior to other algorithms in accuracy and converges faster than others.Besides,the variant of Binary SCLWOA(BSCLWOA)and other binary optimizers obtained by the mapping function was evaluated on 12 UCI data sets.Subsequently,BSCLWOA has proven very competitive in classification precision and feature reduction.