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| Format: | Preprint |
| Published: |
2026
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| Online Access: | https://arxiv.org/abs/2604.25176 |
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| _version_ | 1866914513217912832 |
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| author | Gaikwad, Vijaysinh |
| author_facet | Gaikwad, Vijaysinh |
| contents | The digitization of multi-domain retail billing documents remains a challenging task due to variability in scan quality, layout heterogeneity, and domain diversity across commercial sectors. This paper proposes and benchmarks an intelligent, quality-aware adaptive Optical Character Recognition (OCR) pipeline for retail bill digitization spanning five domains: grocery stores, restaurants, hardware shops, footwear outlets, and clothing retailers. The proposed system integrates a Convolutional Neural Network (CNN)-based image enhancement module trained via self-supervised denoising, a Laplacian variance-based image quality analyzer with three-tier routing, a confidence-driven adaptive feedback loop with iterative retry, and an NLP-based post-OCR correction layer. Experiments were conducted on a real-world dataset of 360 heterogeneous retail bill images. Ground truth for quantitative evaluation was generated using an OCR ensemble majority voting strategy, a validated approach for scenarios without manual annotation. The proposed pipeline achieves a Character Error Rate (CER) of 18.4% and Word Error Rate (WER) of 27.6%, representing improvements of 26.4% and 31.2% respectively over the Raw Tesseract baseline. The pipeline additionally achieves a text density of 108.3 words per image, a noise ratio of 2.3%, and a processing time of 3.64 seconds per image - a 6.4x speed advantage over EasyOCR. Image quality PSNR analysis on enhanced MEDIUM and LOW quality images yields an average of 28.7 dB, confirming meaningful enhancement. These results establish a reproducible benchmark for multi-domain retail bill OCR research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25176 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Benchmarking OCR Pipelines with Adaptive Enhancement for Multi-Domain Retail Bill Digitization Gaikwad, Vijaysinh Computer Vision and Pattern Recognition Machine Learning The digitization of multi-domain retail billing documents remains a challenging task due to variability in scan quality, layout heterogeneity, and domain diversity across commercial sectors. This paper proposes and benchmarks an intelligent, quality-aware adaptive Optical Character Recognition (OCR) pipeline for retail bill digitization spanning five domains: grocery stores, restaurants, hardware shops, footwear outlets, and clothing retailers. The proposed system integrates a Convolutional Neural Network (CNN)-based image enhancement module trained via self-supervised denoising, a Laplacian variance-based image quality analyzer with three-tier routing, a confidence-driven adaptive feedback loop with iterative retry, and an NLP-based post-OCR correction layer. Experiments were conducted on a real-world dataset of 360 heterogeneous retail bill images. Ground truth for quantitative evaluation was generated using an OCR ensemble majority voting strategy, a validated approach for scenarios without manual annotation. The proposed pipeline achieves a Character Error Rate (CER) of 18.4% and Word Error Rate (WER) of 27.6%, representing improvements of 26.4% and 31.2% respectively over the Raw Tesseract baseline. The pipeline additionally achieves a text density of 108.3 words per image, a noise ratio of 2.3%, and a processing time of 3.64 seconds per image - a 6.4x speed advantage over EasyOCR. Image quality PSNR analysis on enhanced MEDIUM and LOW quality images yields an average of 28.7 dB, confirming meaningful enhancement. These results establish a reproducible benchmark for multi-domain retail bill OCR research. |
| title | Benchmarking OCR Pipelines with Adaptive Enhancement for Multi-Domain Retail Bill Digitization |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2604.25176 |