DEEP LEARNING-BASED OCR FRAMEWORK FOR RECEIPTS: PERFORMANCE EVALUATION OF EAST AND CRNN INTEGRATION

Pindonta Ginting, Deo Ekel
Sitorus Pane, Siti Anzani
Nababan, Marlince NK

JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Vol. 11 No. 3 (2026): JITK Issue February 2026

Existing OCR systems often struggle with shopping receipts due to irregular layouts, diverse fonts, and image noise. We propose a domain-specific OCR framework that combines the EAST detector for robust text localisation and the CRNN model for sequence-based recognition. Trained on 320 annotated receipts and tested on 84 images, our system achieved 92.6% character-level and 86.4% word-level accuracy, surpassing Tesseract (+15.2%) and standalone CRNN (+9.7%). These results demonstrate the framework’s effectiveness for receipt-specific OCR, supporting applications such as automated expense tracking and financial record digitisation