Deteksi dan Interpretasi Tulisan Tangan Bahasa Indonesia melalui Pemrosesan Citra dan Optical Character Recognition (OCR) - Susetyo Bagas Bhaskoro; Rizqi Aji Pratama; Shafa Aulia Hazim Darmawan (JTRM (Jurnal Teknologi dan Rekayasa Manufaktur), 2025)
This research develops a handwriting recognition system using OCR based on DB_Resnet and CRNN_VGG16 pre-training architecture integrated with hardware such as Arduino Uno, stepper motor, infrared sensor, camera, and aluminum frame. The system is equipped with a web-based interface developed using Flask. The test shows an average text recognition accuracy of 51.70% with validation using the KBBI dataset. The results show the successful integration of OCR technology with hardware and software, which is expected to increase the efficiency of handwriting data processing.