DEEP LEARNING-BASED OCR FRAMEWORK FOR RECEIPTS: PERFORMANCE EVALUATION OF EAST AND CRNN INTEGRATION
Abstract
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
Fields of Study & Subjects
Handwritten Text Recognition Techniques
Vehicle License Plate Recognition
Advanced Neural Network Applications
Publication details
- Journal / source
- JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer)
- Volume
- 11
- Issue
- 3
- Pages
- 715–723
- Publication date
- 10 February 2026
- Language
- EN