Benchmarking OCR Pipelines with Adaptive Enhancement for Multi-Domain Retail Bill Digitization

• URL: http://arxiv.org/abs/2604.25176v1
• Date: Tue, 28 Apr 2026 03:31:27 GMT
• Title: Benchmarking OCR Pipelines with Adaptive Enhancement for Multi-Domain Retail Bill Digitization
• Abstract summary: This paper proposes and benchmarks an intelligent, quality-aware adaptive Optical Character Recognition pipeline for retail bill digitization.<n> Experiments were conducted on a real-world dataset of 360 heterogeneous retail bill images.<n>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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Beyond OCR Accuracy: Text-Centric VQA Under Image Degradation with Modular and End-to-End [5.790885750156007]
  Text-centric Visual Question Answering (VQA) requires reading and reasoning over text embedded in images.<n>While modular OCR-based pipelines and end-to-end vision-language models are both widely used for this task, their comparative robustness under degraded conditions remains underexplored.<n>We present an empirical study comparing two modular pipelines with SA-DBNet, a custom detector architecture combining ResNet-18 with self-attention spatial modeling and deformable convolutions.
  arXiv  Detail & Related papers  (2026-09-12T08:57:29Z)
• Coupled Continuous-Discrete Generation for Scene Text Image Super-Resolution [51.31474187379532]
  Scene text image super-resolution (STISR) aims to recover visually plausible appearance while preserving character semantics from degraded inputs.<n>We present DualTSR, a unified framework that formulates STISR as coupled continuous-discrete generation.<n> Conditional flow matching restores continuous image latents, while absorbing-state discrete diffusion reconstructs text tokens.
  arXiv  Detail & Related papers  (2026-08-05T06:57:30Z)
• DNA: Dual-stage Native Attribution for Generated Image Source Tracing [75.65470221927096]
  Existing proactive methods rely on watermark embedding or model modification.<n>DNA is a coarse-to-fine framework that follows this hierarchy without additional neural-network training.<n>Experiments show that DNA achieves 89.11% end-to-end attribution accuracy.
  arXiv  Detail & Related papers  (2026-07-15T10:27:52Z)
• Transforming LLMs into Efficient Cross-Encoders via Knowledge Distillation for RAG Reranking [0.0]
  Cross-encoders achieve high reranking accuracy in Retrieval-Augmented Generation (RAG) pipelines but impose quadratic inference costs that limit real-time deployment.<n>We address this by fine-tuning LLaMA 3 (8B) as a drop-in reranker using a two-stage pipeline: supervised fine-tuning on a custom query-document relevance dataset via the Unsloth framework with LoRA adapters, followed by 4-bit quantization for efficient inference.
  arXiv  Detail & Related papers  (2026-07-11T04:31:20Z)
• Understanding vision transformer robustness through the lens of out-of-distribution detection [59.72757235382676]
  Quantization reduces memory and inference costs at the risk of performance loss.<n>We investigate the behaviour of quantized small-variant popular vision transformers (DeiT, DeiT3, and ViT) on common out-of-distribution (OOD) datasets.
  arXiv  Detail & Related papers  (2026-02-01T22:00:59Z)
• Burst Image Quality Assessment: A New Benchmark and Unified Framework for Multiple Downstream Tasks [61.47169388925803]
  We propose a new task of Burst Image Quality Assessment (BuIQA) to evaluate the task-driven quality of each frame within a burst sequence.<n>Inspired by the data analysis, a unified BuIQA framework is proposed to achieve an efficient adaption for BuIQA under diverse downstream scenarios.
  arXiv  Detail & Related papers  (2025-11-11T08:15:52Z)
• E-ARMOR: Edge case Assessment and Review of Multilingual Optical Character Recognition [3.186993645370078]
  In this work, we introduce Sprinklr-Edge-OCR, a novel OCR system built specifically optimized for edge deployment in resource-constrained environments.<n>We present a large-scale comparative evaluation of five state-of-the-art LVLMs (InternVL, Qwen, GOT OCR, LLaMA, MiniCPM) and two traditional OCR systems (Sprinklr-Edge-OCR, SuryaOCR) on a proprietary, doubly hand annotated dataset of multilingual (54 languages) images.<n>Our findings demonstrate that the most optimal OCR systems for edge deployment are the traditional ones due to their low compute requirements, low
  arXiv  Detail & Related papers  (2025-09-03T18:08:41Z)
• DOTA: Deformable Optimized Transformer Architecture for End-to-End Text Recognition with Retrieval-Augmented Generation [0.0]
  This paper introduces a novel end-to-end framework that combines ResNet and Vision Transformer backbones with advanced methodologies, including Deformable Convolutions, Retrieval-Augmented Generation, and Conditional Random Fields (CRF)<n>Experiments conducted on six benchmark datasets establish a new state-of-the-art for text recognition, demonstrating the robustness of the approach across diverse and challenging datasets.
  arXiv  Detail & Related papers  (2025-05-07T07:06:04Z)
• REJEPA: A Novel Joint-Embedding Predictive Architecture for Efficient Remote Sensing Image Retrieval [11.105541122596003]
  REJEPA (Retrieval with Joint-Embedding Predictive Architecture) is an innovative self-supervised framework designed for unimodal RS-CBIR.<n>It uses spatially distributed context token encoding to forecast abstract representations of target tokens.<n>It achieves a reduction in computational complexity of 40-60% when compared to pixel-reconstruction baselines.
  arXiv  Detail & Related papers  (2025-04-04T04:59:10Z)
• How Quality Affects Deep Neural Networks in Fine-Grained Image Classification [0.799543372823325]
  We propose a No-Reference Image Quality Assessment (NRIQA) guided cut-off point selection (CPS) strategy to enhance the performance of a fine-grained classification system. We take the three most commonly adopted image augmentation configurations -- cropping, rotating, and blurring -- as the entry point. Concretely, the cut-off points yielded by those methods are aggregated via majority voting to inform the process of image subset selection.
  arXiv  Detail & Related papers  (2024-05-09T12:59:11Z)
• LOCR: Location-Guided Transformer for Optical Character Recognition [55.195165959662795]
  We propose LOCR, a model that integrates location guiding into the transformer architecture during autoregression. We train the model on a dataset comprising over 77M text-location pairs from 125K academic document pages, including bounding boxes for words, tables and mathematical symbols. It outperforms all existing methods in our test set constructed from arXiv, as measured by edit distance, BLEU, METEOR and F-measure.
  arXiv  Detail & Related papers  (2024-03-04T15:34:12Z)
• BAND-2k: Banding Artifact Noticeable Database for Banding Detection and Quality Assessment [52.1640725073183]
  Banding, also known as staircase-like contours, frequently occurs in flat areas of images/videos processed by the compression or quantization algorithms. We build the largest banding IQA database so far, named Banding Artifact Noticeable Database (BAND-2k), which consists of 2,000 banding images. A dual convolutional neural network is employed to concurrently learn the feature representation from the high-frequency and low-frequency maps.
  arXiv  Detail & Related papers  (2023-11-29T15:56:31Z)
• Recurrence With Correlation Network for Medical Image Registration [66.63200823918429]
  We present Recurrence with Correlation Network (RWCNet), a medical image registration network with multi-scale features and a cost volume layer. We demonstrate that these architectural features improve medical image registration accuracy in two image registration datasets.
  arXiv  Detail & Related papers  (2023-02-05T02:41:46Z)
• Distortion-Aware Loop Filtering of Intra 360^o Video Coding with Equirectangular Projection [81.63407194858854]
  We propose a distortion-aware loop filtering model to improve the performance of intra coding for 360$o$ videos projected via equirectangular projection (ERP) format. Our proposed module analyzes content characteristics based on a coding unit (CU) partition mask and processes them through partial convolution to activate the specified area.
  arXiv  Detail & Related papers  (2022-02-20T12:00:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.