The handwritten digit recognition issue turns into one of the well-known issues in machine learning and computer vision applications. Numerous machine learning methods have been utilized to resolve the handwritten digit recognition problem. However, sometimes the digit is not completely present in the image due to issues related to scanning or environmental conditions (light, illumination, dirt, etc.). Although different efficient methodologies of handwritten digit recognition are proposed, there is not much work done on fragmented handwritten digit recognition. The objective of the proposed research work is to handle this circumstance to assemble a consistent digit recognition system that can precisely handle three types (English, Bangla, and Devanagari) of fragmented handwritten digit images. To solve the confusion, a technique is created to classify handwritten digits based on geometrical functions that are utilized to calculate handwritten digit features to assess if a digit belongs to a specific class. A grading scheme and a set of specified fuzzy rules determine the performance of classification. Experiments have been directed on the three familiar datasets, i.e., MNIST database (English), NumtaDB (Bangla) and Deva numeral database (Devanagari). Since fragmented digit delivers a lesser amount of information, the work also attempts to create a tentative size threshold above which outcomes become erratic and whether such thresholds are standardized or vary depending on other factors. Since the fragmented handwritten digital image does not have a public database, a method is formed to produce repeatable fragmented handwritten digital images from the entire image. Experimental outcomes validate that the proposed approach is effective in recognizing fragmented handwritten digits to an acceptable degree of fragmentation.

手写数字识别问题已成为机器学习和计算机视觉应用程序中的著名问题之一。已经利用多种机器学习方法来解决手写数字识别问题。但是，有时由于与扫描或环境条件（光线，照明，灰尘等）有关的问题，手指可能不会完全出现在图像中。尽管提出了不同的手写数字识别有效方法，但是在零碎的手写数字识别上所做的工作并不多。拟议中的研究工作的目的是要处理这种情况，以组装出一个一致的数字识别系统，该系统可以精确处理三种类型的（零碎的）手写数字图像（英语，孟加拉语和梵文）。为了解决困惑，创建了一种基于几何函数对手写数字进行分类的技术，该几何函数用于计算手写数字特征以评估数字是否属于特定类别。分级方案和一组指定的模糊规则确定分类的性能。实验针对三个熟悉的数据集，即MNIST数据库（英语），NumtaDB（孟加拉）和Deva数字数据库（Devanagari）。由于零散的数字传递的信息量较少，因此该工作还尝试创建一个暂定的大小阈值，超过该阈值，结果将变得不稳定，并且该阈值是否已标准化或取决于其他因素。由于碎片化的手写数字图像没有公共数据库，形成了一种从整个图像中产生可重复的，片段化的手写数字图像的方法。实验结果验证了所提出的方法可以有效地将碎片化的手写数字识别到可接受的碎片化程度。