A visual segmentation method for off-line handwritten Uyghur text

Visual segmentation based on the meaning of Uyghur text is an important process of dynamic Uyghur character retrieval, It also provides great convenience for workers who learn and understand the Uygur language for the first time. To address the challenges of strong continuity and high character similarity in handwritten Uyghur text, which make effective segmentation difficult, we propose an efficient and lightweight model for handwritten Uyghur text Detection segmentation, based on the Deeplabv3+ network in convolutional neural networks. Our experiment selects 100 common words in Uyghur language, and collects a total of 7093 handwritten samples. We use median filtering, adaptive threshold binarization and other operations to standardize and denoise the samples to achieve scientific preprocessing. We use the MobileNetV2 module by replacing the original Xception module in the model, to guarantee precision, significantly reducing training parameters and saving training time. To solve potential positive and negative sample imbalance in data, we use Focal loss and Dice loss. Moreover, we introduce CBAM attention mechanism in the deep feature extraction network to enhance the ability of deep feature extraction and refine important text features. Our experimental results demonstrate that the proposed algorithm significantly improves segmentation accuracy while meeting performance requirements, shortening training time, and enhancing the segmentation effect of handwritten Uyghur text, improve readability of split text.