# Automated document processing system for government agencies using DBNET++ and BART models

arXiv: https://arxiv.org/abs/2510.13303 · HTML: https://mlsanity.com/paper/2510.13303/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://mlsanity.com/paper/2204.08387/) (2022)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition](https://mlsanity.com/paper/1710.10400/) (2017)
- [Scene Text Detection and Recognition: The Deep Learning Era](https://mlsanity.com/paper/1811.04256/) (2018)
- [Real-Time Scene Text Detection with Differentiable Binarization and Adaptive Scale Fusion](https://mlsanity.com/paper/2202.10304/) (2022)
- [A Survey on Text Classification: From Shallow to Deep Learning](https://mlsanity.com/paper/2008.00364/) (2020)
- [Adaptive Boundary Proposal Network for Arbitrary Shape Text Detection](https://mlsanity.com/paper/2107.12664/) (2021)
- [TextDCT: Arbitrary-Shaped Text Detection via Discrete Cosine Transform Mask](https://mlsanity.com/paper/2206.13381/) (2022)
- [Real-time Scene Text Detection Based on Global Level and Word Level Features](https://mlsanity.com/paper/2203.05251/) (2022)

## Abstract (arXiv)

An automatic document classification system is presented that detects textual content in images and classifies documents into four predefined categories (Invoice, Report, Letter, and Form). The system supports both offline images (e.g., files on flash drives, HDDs, microSD) and real-time capture via connected cameras, and is designed to mitigate practical challenges such as variable illumination, arbitrary orientation, curved or partially occluded text, low resolution, and distant text. The pipeline comprises four stages: image capture and preprocessing, text detection [1] using a DBNet++ (Differentiable Binarization Network Plus) detector, and text classification [2] using a BART (Bidirectional and Auto-Regressive Transformers) classifier, all integrated within a user interface implemented in Python with PyQt5. The achieved results by the system for text detection in images were good at about 92.88% through 10 hours on Total-Text dataset that involve high resolution images simulate a various and very difficult challenges. The results indicate the proposed approach is effective for practical, mixed-source document categorization in unconstrained imaging scenarios.