Comparative Analysis of CNN-Based Frameworks for Handwritten Arabic Numerals Recognition

The Arabic Language is the broadest and most used Language in the Arab World, with more than 423 million people. There are various representations in the Arabic Number system like Hindu-Arabic, Mashriqi Arabic, Eastern Arabic, and Western Arabic, which are look-a-like. Handwritten Character Recognition or Optical Character Recognition has gained popularity in recent days. This paper focuses on the importance of Optical Character Recognition (OCR) and different CNN-Based frameworks such as Lenet-5, ResNet-101, and ResNet-152 available today for OCR. This research study has created a dataset with more than 50,000 training and 7000 testing images of Arabic numbers ranging from 1 to 100 numbers written by various age groups and named it LBREADB. After that, various CNN-Based frameworks were trained with LBREADB and tested on the random inputs. A comparison has been made among the results based on the measurement criteria such as accuracy and prediction rate, which were shown at the ending.