Identifying writers using their handwriting is particularly challenging for a machine, given that a person’s writing can serve as their distinguishing characteristic. The process of identification using handcrafted features has shown promising results, but the intra-class variability between authors still needs further development. Almost all computer vision-related tasks use Deep learning (DL) nowadays, and as a result, researchers are developing many DL architectures with their respective methods. In addition, feature extraction, usually accomplished using handcrafted algorithms, can now be automatically conducted using convolutional neural networks. With the various developments of the DL method, it is necessary to evaluate the suitable DL for the problem we are aiming at, namely the classification of writer identification. This comparative study evaluated several DL architectures such as VGG16, ResNet50, MobileNet, Xception, and EfficientNet end-to-end to examine their advantages to offline handwriting for writer identification problems with IAM and CVL databases. Each architecture compared its respective process to the training and validation metrics accuracy, demonstrating that ResNet50 DL had the highest train accuracy of 98.86%. However, Xception DL performed slightly better due to the convergence gap for validation accuracy compared to all the other architectures, which were 21.79% and 15.12% for IAM and CVL. Also, the smallest gap of convergence between training and validation accuracy for the IAM and CVL datasets were 19.13% and 16.49%, respectively. The results of these findings serve as the basis for DL architecture selection and open up overfitting problems for future work.
Berdasarkan instruksi dari Badan Akreditasi Nasional Pendidikan Anak Usia Dini (BAN-PAUD), salah alat yang harus disediakan oleh pihak sekolah adalah Buku Penghubung, sebagai media untuk monitoring dan pelaporan tumbuh kembang siswa kepada orang tuanya. Sekolah Alam Gaharu (SAG) Bandung telah menggunakan Buku Penghubung, namun sistemnya masih tergolong konvensional. Pada sistem konvensional ini ditemukan permasalahan-permasalahan yang mengakibatkan ketidakselarasan antara wali murid dan guru/fasilitator kelas dalam hal monitoring tumbuh kembang anak, seperti: buku yang rentan rusak atau hilang, kurang privasi, tidak real-time dan tidak dapat mengakomodasi file foto/video kegiatan. Dengan kemajuan teknologi pada era sekarang, memungkinkan dilakukan transformasi buku penghubung berbasis kertas menjadi bentuk aplikasi digital berbasis website maupun Android/iOS. Untuk itu, pada kegiatan pengabdian masyarakat ini dikenalkan sebuah aplikasi Buku Penghubung Digital bernama Kids Note. Pelatihan diberikan kepada 50 peserta yang terdiri dari orang tua/wali murid dan guru/fasilitator di Sekolah Alam Gaharu. Berdasarkan survei yang dibagikan pasca kegiatan, didapatkan hasil bahwa 84% peserta memahami cara penggunaan aplikasi Kids Note dan 92% diantaranya menyatakan aplikasi Kids Note mampu mengakomodasi kebutuhan monitoring dan pelaporan tumbuh kembang anak di SAG.
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