# Deep Learning for Technical Document Classification

arXiv: https://arxiv.org/abs/2106.14269 · HTML: https://mlsanity.com/paper/2106.14269/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 117 references that are in the map.
- First posted on arXiv 27 Jun 2021.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [Recurrent Neural Network for Text Classification with Multi-Task Learning](https://mlsanity.com/paper/1605.05101/) (2016)
- [Ask Me Anything: Dynamic Memory Networks for Natural Language Processing](https://mlsanity.com/paper/1506.07285/) (2015)
- [Deep Multimodal Learning for Audio-Visual Speech Recognition](https://mlsanity.com/paper/1501.05396/) (2015)
- [Multimodal deep networks for text and image-based document classification](https://mlsanity.com/paper/1907.06370/) (2019)
- [Data-Driven Design-by-Analogy: State of the Art and Future Directions](https://mlsanity.com/paper/2106.01592/) (2021)
- [Engineering Knowledge Graph from Patent Database](https://mlsanity.com/paper/2106.06739/) (2021)
- [TechNet: Technology Semantic Network Based on Patent Data](https://mlsanity.com/paper/1906.00411/) (2019)
- [A Data Driven Approach for Compound Figure Separation Using Convolutional Neural Networks](https://mlsanity.com/paper/1703.05105/) (2017)
- [Wikipedia2Vec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from Wikipedia](https://mlsanity.com/paper/1812.06280/) (2018)
- [Automatic Separation of Compound Figures in Scientific Articles](https://mlsanity.com/paper/1606.01021/) (2016)

## Built on by (most cited first, 11 of 11)

- [CAD-Coder: An Open-Source Vision-Language Model for Computer-Aided Design Code Generation](https://mlsanity.com/paper/2505.14646/) (2025)
- [DesignQA: A Multimodal Benchmark for Evaluating Large Language Models' Understanding of Engineering Documentation](https://mlsanity.com/paper/2404.07917/) (2024)
- [Multi-modal Machine Learning in Engineering Design: A Review and Future Directions](https://mlsanity.com/paper/2302.10909/) (2023)
- [Natural Language Processing in the Patent Domain: A Survey](https://mlsanity.com/paper/2403.04105/) (2024)
- [Natural Language Processing in-and-for Design Research](https://mlsanity.com/paper/2111.13827/) (2021)
- [A Critical Review of Large Language Models: Sensitivity, Bias, and the Path Toward Specialized AI](https://mlsanity.com/paper/2307.15425/) (2023)
- [Toward Artificial Empathy for Human-Centered Design: A Framework](https://mlsanity.com/paper/2303.10583/) (2023)
- [Patent Data for Engineering Design: A Critical Review and Future Directions](https://mlsanity.com/paper/2111.08500/) (2021)
- [DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding](https://mlsanity.com/paper/2311.04098/) (2023)
- [Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification](https://mlsanity.com/paper/2310.20478/) (2023)
- [Document Classification Pattern Recognition via Information Fusion: A Systematic Review of Multimodal and Multiview Representation Approaches](https://mlsanity.com/paper/2605.23910/) (2026)

## Abstract (arXiv)

In large technology companies, the requirements for managing and organizing technical documents created by engineers and managers have increased dramatically in recent years, which has led to a higher demand for more scalable, accurate, and automated document classification. Prior studies have only focused on processing text for classification, whereas technical documents often contain multimodal information. To leverage multimodal information for document classification to improve the model performance, this paper presents a novel multimodal deep learning architecture, TechDoc, which utilizes three types of information, including natural language texts and descriptive images within documents and the associations among the documents. The architecture synthesizes the convolutional neural network, recurrent neural network, and graph neural network through an integrated training process. We applied the architecture to a large multimodal technical document database and trained the model for classifying documents based on the hierarchical International Patent Classification system. Our results show that TechDoc presents a greater classification accuracy than the unimodal methods and other state-of-the-art benchmarks. The trained model can potentially be scaled to millions of real-world multimodal technical documents, which is useful for data and knowledge management in large technology companies and organizations.