# CloudScan - A configuration-free invoice analysis system using recurrent neural networks

arXiv: https://arxiv.org/abs/1708.07403 · HTML: https://mlsanity.com/paper/1708.07403/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2017.

## Built on by (most cited first, 30 of 33)

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- [FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction](https://mlsanity.com/paper/2203.08411/) (2022)
- [Spatial Dual-Modality Graph Reasoning for Key Information Extraction](https://mlsanity.com/paper/2103.14470/) (2021)
- [MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding](https://mlsanity.com/paper/2108.06543/) (2021)
- [LMDX: Language Model-based Document Information Extraction and Localization](https://mlsanity.com/paper/2309.10952/) (2023)
- [ViBERTgrid: A Jointly Trained Multi-Modal 2D Document Representation for Key Information Extraction from Documents](https://mlsanity.com/paper/2105.11672/) (2021)
- [ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction](https://mlsanity.com/paper/2106.10786/) (2021)
- [FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction](https://mlsanity.com/paper/2305.02549/) (2023)
- [DocParser: End-to-end OCR-free Information Extraction from Visually Rich Documents](https://mlsanity.com/paper/2304.12484/) (2023)
- [Cost-effective End-to-end Information Extraction for Semi-structured Document Images](https://mlsanity.com/paper/2104.08041/) (2021)
- [Business Document Information Extraction: Towards Practical Benchmarks](https://mlsanity.com/paper/2206.11229/) (2022)
- [Information Extraction from Visually Rich Documents using LLM-based Organization of Documents into Independent Textual Segments](https://mlsanity.com/paper/2505.13535/) (2025)
- [End-to-End Page-Level Assessment of Handwritten Text Recognition](https://mlsanity.com/paper/2301.05935/) (2023)
- [End-to-End Hierarchical Relation Extraction for Generic Form Understanding](https://mlsanity.com/paper/2106.00980/) (2021)
- [QueryForm: A Simple Zero-shot Form Entity Query Framework](https://mlsanity.com/paper/2211.07730/) (2022)
- [GMN: Generative Multi-modal Network for Practical Document Information Extraction](https://mlsanity.com/paper/2207.04713/) (2022)
- [TRIE++: Towards End-to-End Information Extraction from Visually Rich Documents](https://mlsanity.com/paper/2207.06744/) (2022)
- [Deep Learning based Key Information Extraction from Business Documents: Systematic Literature Review](https://mlsanity.com/paper/2408.06345/) (2024)
- [Using Neighborhood Context to Improve Information Extraction from Visual Documents Captured on Mobile Phones](https://mlsanity.com/paper/2108.10395/) (2021)
- [Information Extraction from Scanned Invoice Images using Text Analysis and Layout Features](https://mlsanity.com/paper/2208.04011/) (2022)
- [XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser](https://mlsanity.com/paper/2405.17336/) (2024)
- [UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents](https://mlsanity.com/paper/2602.07038/) (2026)
- [Improving Information Extraction on Business Documents with Specific Pre-Training Tasks](https://mlsanity.com/paper/2309.05429/) (2023)
- [ExTTNet: A Deep Learning Algorithm for Extracting Table Texts from Invoice Images](https://mlsanity.com/paper/2402.02246/) (2024)
- [TransDocAnalyser: A Framework for Offline Semi-structured Handwritten Document Analysis in the Legal Domain](https://mlsanity.com/paper/2306.02142/) (2023)
- [DocILE 2023 Teaser: Document Information Localization and Extraction](https://mlsanity.com/paper/2301.12394/) (2023)
- [An agentic system with reinforcement-learned subsystem improvements for parsing form-like documents](https://mlsanity.com/paper/2505.13504/) (2025)
- [Enhancing Large Multimodal Models in Key Information Extraction via Scene-Aware Document Synthesis](https://mlsanity.com/paper/2607.04636/) (2026)

## Abstract (arXiv)

We present CloudScan; an invoice analysis system that requires zero configuration or upfront annotation. In contrast to previous work, CloudScan does not rely on templates of invoice layout, instead it learns a single global model of invoices that naturally generalizes to unseen invoice layouts. The model is trained using data automatically extracted from end-user provided feedback. This automatic training data extraction removes the requirement for users to annotate the data precisely. We describe a recurrent neural network model that can capture long range context and compare it to a baseline logistic regression model corresponding to the current CloudScan production system. We train and evaluate the system on 8 important fields using a dataset of 326,471 invoices. The recurrent neural network and baseline model achieve 0.891 and 0.887 average F1 scores respectively on seen invoice layouts. For the harder task of unseen invoice layouts, the recurrent neural network model outperforms the baseline with 0.840 average F1 compared to 0.788.