Development of a Benchmark Odia Handwritten Character Database for an Efficient Offline Handwritten Character Recognition with a Chronological Survey

Abstract

A good benchmark dataset is a primary requirement in the offline handwritten character recognition (HCR) process. Only three handwritten numerals and alphabet datasets from Odia are publicly accessible for study, although many writers have used several datasets in their experiments. In this article, two tasks are done to address this issue. Those are the following: First, an extensive survey focused on various datasets is provided with the methodologies used in chronological order. The second factor is a solution to the lack of publicly available handwritten characters and numeral datasets. A new dataset of handwritten Odia characters with numerals has been developed. Anyone can access this dataset by sending an email to the authors of the article. This dataset was created with the help of 150 volunteers of various age groups, races, and qualifications. Some homogeneous experiments are conducted using deep learning models to evaluate the consistency of the dataset. One heterogeneous trial has also been performed to estimate the complexities of the characters present in the dataset by comparing them with the existing benchmark datasets.