DNLL dataset

The Numerical Latin Letters (DNLL) dataset comprises Latin numeric letters, organized into 26 distinct letter classes corresponding to the Latin alphabet. Within this dataset, each class encompasses multiple letter forms, resulting in a diverse and extensive collection. These letters vary in terms of color, size, writing style, thickness, background, orientation, luminosity, and other attributes, rendering the dataset highly comprehensive and rich.DNLL exclusively includes isolated letters, and it is divided into three essential files: training, testing, and validation. This division not only facilitates text detection and recognition tasks but also ensures robust and accurate results. The dataset is distributed as follows: the training set comprises 80% of the total images, while the remaining images are split between the testing set (80% of the remaining images) and the validation set (20% of the remaining images).In the processing stage, the images and data undergo enhancement and augmentation procedures to further enrich the dataset and optimize its quality.

数字拉丁字母（DNLL）数据集收录了拉丁数字字母，按照拉丁字母表划分为26个独立的字母类别。该数据集的每个类别均包含多种字母形态，整体构成了多样化且体量庞大的样本集合。这些字母在色彩、尺寸、书写风格、笔画粗细、背景样式、朝向、亮度等诸多属性上存在差异，使得该数据集具备极强的全面性与丰富度。DNLL仅包含孤立字母样本，并划分为训练集、测试集与验证集三个核心文件。这种划分方式不仅便于开展文本检测与识别相关任务，同时也能保障模型获得稳健且精准的实验结果。数据集的样本分配规则如下：训练集包含总样本量的80%，剩余样本中80%划入测试集，余下20%划入验证集。在数据处理阶段，研究人员会对图像与原始数据执行图像增强与数据扩增操作，以进一步丰富数据集内容并优化其整体质量。