Convolutional-Neural-Network-Based Handwritten Character Recognition: An Approach with Massive Multisource Data

Abstract

Neural networks have made big strides in image classification. Convolutional neural networks (CNN) work successfully to run neural networks on direct images. Handwritten character recognition (HCR) is now a very powerful tool to detect traffic signals, translate language, and extract information from documents, etc. Although handwritten character recognition technology is in use in the industry, present accuracy is not outstanding, which compromises both performance and usability. Thus, the character recognition technologies in use are still not very reliable and need further improvement to be extensively deployed for serious and reliable tasks. On this account, characters of the English alphabet and digit recognition are performed by proposing a custom-tailored CNN model with two different datasets of handwritten images, i.e., Kaggle and MNIST, respectively, which are lightweight but achieve higher accuracies than state-of-the-art models. The best two models from the total of twelve designed are proposed by altering hyper-parameters to observe which models provide the best accuracy for which dataset. In addition, the classification reports (CRs) of these two proposed models are extensively investigated considering the performance matrices, such as precision, recall, specificity, and F1 score, which are obtained from the developed confusion matrix (CM). To simulate a practical scenario, the dataset is kept unbalanced and three more averages for the F measurement (micro, macro, and weighted) are calculated, which facilitates better understanding of the performances of the models. The highest accuracy of 99.642% is achieved for digit recognition, with the model using ‘RMSprop’, at a learning rate of 0.001, whereas the highest detection accuracy for alphabet recognition is 99.563%, which is obtained with the proposed model using ‘ADAM’ optimizer at a learning rate of 0.00001. The macro F1 and weighted F1 scores for the best two models are 0.998, 0.997:0.992, and 0.996, respectively, for digit and alphabet recognition.

Handwritten Recognition Techniques: A Comprehensive Review

“…Saqib et al [30] presented an uncommonly outlined CNN show for written-by-hand character recognition (HCR) utilizing two diverse datasets. The article proposed ideal CNN models and surveyed their execution employing an assortment of criteria in an effort to extend the precision of HCR frameworks.…”

Character Level Segmentation and Recognition using CNN Followed Random Forest Classifier for NPR System

“…The characters such as -O -0, I -1, B -8, C -G, A -4, D -0, D -O, G -6, and 2 -Z‖ are close enough that character recognizers may get them mixed up. All of these flaws should be addressed by character recognition algorithms [1,10,20]. In spite of the increased emphasis given to character recognition in ANPR systems, the ambiguous character problem remains a major issue.…”

“…The ANPR system comprises different phases as seen in Fig. 1: (1) image acquisition (2) number plate identification and extraction (3) character segmentation and (4) character recognition [1]. The first two phases identify and capture an image of the vehicle.…”

Telugu letters dataset and parallel deep convolutional neural network with a SGD optimizer model for TCR

“…These characteristics subsequently impact recognition. Characters may appear broken or touching depending on the type of paper used, especially when printed documents are involved [5].…”