Hybrid neural models for automatic handwritten digits recognition
Aline A. Peres, Susana Margarida Vieira, J.R. Caldas Pinto · 2018
In this paper a novel Handwritten Character Identification methodology that performs the recognition of the students identification numbers handwritten in classroom maps has been proposed. A dataset of 60.0000 handwritten training images of the MNIST dataset and a proprietary dataset composed of 3.415 images extracted from 12 classroom maps handwritten by 11 different persons is used in this work. These images were obtained through the segmentation process described in this work, and had suffer some image processing before feature extraction and classification in order to be as similar as possible to the MNIST dataset samples. The algorithm is composed by four main steps: pre-processing, segmentation, feature extraction and classification. It was implemented a segmentation strategy designed for the classroom maps, based on morphological operations and connected components. This strategy can be easily adapted to others handwritten character recognition problems. In classification, three different approaches are used: Support Vector Machine, Convolutional Neural Networks and an hybrid approach that use the CNN extracted features as inputs for a Support Vector Machine. The hybrid approach has the advantage of avoiding the feature extraction step. The dataset is divided into train, test and validation sets and the performance of the three different classifiers is compared and evaluated. The results presented were obtained using real classroom maps. The hybrid CNN+SVM classifier achieved a high performance with overall accuracy of 96.5%. In conclusion, this designed system could successfully reduce the time of creating and accessing the digital storage of students examinations, as well as allow the automatic indexing of student grades towards a full digital evaluation system.