Handprinted Character and Online Signature Recognition Using Residual Convolutional Network: A Comparative Study
Hadi Mohsen Oqaibi, Abdullah Ahmad Basuhail, Gibrael Abosamra · 2021 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) · 2021
Residual convolutional networks proved superiority in the image recognition field in addition to many other pattern recognitions related problems. A special benefit of residual convolutional networks has been proven in the field of handwriting recognition, especially in handprinted characters and online signature recognition. Two published research papers addressed the two problems with highly accurate results that compete with the state-of-the-art techniques. Both problems have two well-known challenges in object classification: intra-class variability and inter-class similarity. In the character recognition problem, different users produce different shapes for the same character in addition to the user himself, who produces different shapes for the same character resulting in high intra-class variability. Inter-class similarity exists due to the similarity between different characters. In handwritten signature recognition, intra- class variability is introduced by the same person where no two signatures of the same person may coincide. On the other hand, persons may have similar signatures resulting in inter-class similarity. Solving the two pattern recognition problems using deep learning techniques requires the selection of the best technique that can model the handwriting process, but at the same time, it requires a large training data. In the character recognition case, large training samples are available from many datasets resources. A recent data set representing this challenge is the EMINST dataset. An optimized residual architecture has been introduced to give an excellent solution for this problem in one of the comparison papers. In the case of signature recognition, a different solution based on a residual network has been introduced in a second paper because practically training samples that can be collected from new users are very rare. In this paper, the two techniques are compared, and conclusions that may generalize well in other problem domains are stated.