DEEP LEARNING-BASED OCR FRAMEWORK FOR RECEIPTS: PERFORMANCE EVALUATION OF EAST AND CRNN INTEGRATION (JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer), 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
Keywords: CRNN; Optimising; Text Detection; Text Extraction