# Delivering Document Conversion as a Cloud Service with High Throughput and Responsiveness

arXiv: https://arxiv.org/abs/2206.00785 · HTML: https://mlsanity.com/paper/2206.00785/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 1 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [Global Table Extractor (GTE): A Framework for Joint Table Identification and Cell Structure Recognition Using Visual Context](https://mlsanity.com/paper/2005.00589/) (2020)
- [TableFormer: Table Structure Understanding with Transformers](https://mlsanity.com/paper/2203.01017/) (2022)
- [Extracting Scientific Figures with Distantly Supervised Neural Networks](https://mlsanity.com/paper/1804.02445/) (2018)
- [Corpus Conversion Service: A Machine Learning Platform to Ingest Documents at Scale](https://mlsanity.com/paper/1806.02284/) (2018)
- [Robust PDF Document Conversion Using Recurrent Neural Networks](https://mlsanity.com/paper/2102.09395/) (2021)
- [An Information Extraction and Knowledge Graph Platform for Accelerating Biochemical Discoveries](https://mlsanity.com/paper/1907.08400/) (2019)

## Built on by (most cited first, 7 of 7)

- [SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion](https://mlsanity.com/paper/2503.11576/) (2025)
- [Optimized Table Tokenization for Table Structure Recognition](https://mlsanity.com/paper/2305.03393/) (2023)
- [Docling Technical Report](https://mlsanity.com/paper/2408.09869/) (2024)
- [FETA: Towards Specializing Foundation Models for Expert Task Applications](https://mlsanity.com/paper/2209.03648/) (2022)
- [IndicDLP: A Foundational Dataset for Multi-Lingual and Multi-Domain Document Layout Parsing](https://mlsanity.com/paper/2512.20236/) (2025)
- [MaterioMiner -- An ontology-based text mining dataset for extraction of process-structure-property entities](https://mlsanity.com/paper/2408.04661/) (2024)
- [Extracting ontology-compliant knowledge from scientific text describing irradiated materials using large language models](https://mlsanity.com/paper/2609.17291/) (2026)

## Abstract (arXiv)

Document understanding is a key business process in the data-driven economy since documents are central to knowledge discovery and business insights. Converting documents into a machine-processable format is a particular challenge here due to their huge variability in formats and complex structure. Accordingly, many algorithms and machine-learning methods emerged to solve particular tasks such as Optical Character Recognition (OCR), layout analysis, table-structure recovery, figure understanding, etc. We observe the adoption of such methods in document understanding solutions offered by all major cloud providers. Yet, publications outlining how such services are designed and optimized to scale in the cloud are scarce. In this paper, we focus on the case of document conversion to illustrate the particular challenges of scaling a complex data processing pipeline with a strong reliance on machine-learning methods on cloud infrastructure. Our key objective is to achieve high scalability and responsiveness for different workload profiles in a well-defined resource budget. We outline the requirements, design, and implementation choices of our document conversion service and reflect on the challenges we faced. Evidence for the scaling behavior and resource efficiency is provided for two alternative workload distribution strategies and deployment configurations. Our best-performing method achieves sustained throughput of over one million PDF pages per hour on 3072 CPU cores across 192 nodes.