# DocVLM: Make Your VLM an Efficient Reader

arXiv: https://arxiv.org/abs/2412.08746 · HTML: https://mlsanity.com/paper/2412.08746/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 57 of its 62 references that are in the map.
- First posted on arXiv 11 Dec 2024.

## Placed in

- Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/)

## Builds on (most cited first, 30 of 57)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://mlsanity.com/paper/2409.12191/) (2024)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [Towards VQA Models That Can Read](https://mlsanity.com/paper/1904.08920/) (2019)
- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [DocVQA: A Dataset for VQA on Document Images](https://mlsanity.com/paper/2007.00398/) (2020)
- [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://mlsanity.com/paper/2306.14824/) (2023)
- [mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality](https://mlsanity.com/paper/2304.14178/) (2023)
- [PaliGemma: A versatile 3B VLM for transfer](https://mlsanity.com/paper/2407.07726/) (2024)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models](https://mlsanity.com/paper/2305.07895/) (2023)
- [InfographicVQA](https://mlsanity.com/paper/2104.12756/) (2021)
- [TextCaps: a Dataset for Image Captioning with Reading Comprehension](https://mlsanity.com/paper/2003.12462/) (2020)
- [FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents](https://mlsanity.com/paper/1905.13538/) (2019)
- [Scene Text Visual Question Answering](https://mlsanity.com/paper/1905.13648/) (2019)
- [What matters when building vision-language models?](https://mlsanity.com/paper/2405.02246/) (2024)
- [DocFormer: End-to-End Transformer for Document Understanding](https://mlsanity.com/paper/2106.11539/) (2021)
- [PaLI-X: On Scaling up a Multilingual Vision and Language Model](https://mlsanity.com/paper/2305.18565/) (2023)
- [InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output](https://mlsanity.com/paper/2407.03320/) (2024)
- [TokenPacker: Efficient Visual Projector for Multimodal LLM](https://mlsanity.com/paper/2407.02392/) (2024)
- [InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD](https://mlsanity.com/paper/2404.06512/) (2024)
- [UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model](https://mlsanity.com/paper/2310.05126/) (2023)

## Built on by (most cited first, 28 of 28)

- [FineVision: Open Data Is All You Need](https://mlsanity.com/paper/2510.17269/) (2025)
- [Multimodal Large Language Models for Text-rich Image Understanding: A Comprehensive Review](https://mlsanity.com/paper/2502.16586/) (2025)
- [Unlocking Multimodal Document Intelligence: From Current Triumphs to Future Frontiers of Visual Document Retrieval](https://mlsanity.com/paper/2602.19961/) (2026)
- [Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understanding](https://mlsanity.com/paper/2603.18472/) (2026)
- [SCAN: Semantic Document Layout Analysis for Textual and Visual Retrieval-Augmented Generation](https://mlsanity.com/paper/2505.14381/) (2025)
- [UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents](https://mlsanity.com/paper/2602.07038/) (2026)
- [Hidden Tail: Adversarial Image Causing Stealthy Resource Consumption in Vision-Language Models](https://mlsanity.com/paper/2508.18805/) (2025)
- [Towards Reliable and Interpretable Document Question Answering via VLMs](https://mlsanity.com/paper/2509.10129/) (2025)
- [DocSLM: A Small Vision-Language Model for Long Multimodal Document Understanding](https://mlsanity.com/paper/2511.11313/) (2025)
- [DODO: Discrete OCR Diffusion Models](https://mlsanity.com/paper/2602.16872/) (2026)
- [Hierarchical Evidence-Driven Reasoning for Long Document Understanding](https://mlsanity.com/paper/2607.04625/) (2026)
- [Exploring OCR-augmented Generation for Bilingual VQA](https://mlsanity.com/paper/2510.02543/) (2025)
- [From Dead Pixels to Editable Slides: Infographic Reconstruction into Native Google Slides via Vision-Language Region Understanding](https://mlsanity.com/paper/2602.07645/) (2026)
- [Can Vision-Language Models See Squares? Text-Recognition Mediates Spatial Reasoning Across Three Model Families](https://mlsanity.com/paper/2602.15950/) (2026)
- [Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production](https://mlsanity.com/paper/2605.18818/) (2026)
- [Bridging Vision, Language, and Mathematics: Pictographic Character Reconstruction with B'ezier Curves](https://mlsanity.com/paper/2511.00076/) (2025)
- [Optimizing Multimodal Language Models through Attention-based Interpretability](https://mlsanity.com/paper/2511.23375/) (2025)
- [Spatially-Grounded Document Retrieval via Patch-to-Region Relevance Propagation](https://mlsanity.com/paper/2512.02660/) (2025)
- [Evaluating VLMs' Spatial Reasoning Over Robot Motion: A Step Towards Robot Planning with Motion Preferences](https://mlsanity.com/paper/2603.13100/) (2026)
- [Evo-Retriever: LLM-Guided Curriculum Evolution with Viewpoint-Pathway Collaboration for Multimodal Document Retrieval](https://mlsanity.com/paper/2603.16455/) (2026)
- [DISCO: Document Intelligence Suite for COmparative Evaluation](https://mlsanity.com/paper/2603.23511/) (2026)
- [A Dataset for Dynamic Human Preferences for Vision Language Models](https://mlsanity.com/paper/2606.07653/) (2026)
- [Edges Before Embeddings: A Confidence-Aware Blur Gate for Vision-Language Pipelines](https://mlsanity.com/paper/2606.25838/) (2026)
- [MatPhaseBench: A Semantics-Guided Benchmark for Materials Phase Diagrams Understanding](https://mlsanity.com/paper/2607.02934/) (2026)
- [State-Conditioned Visual Evidence Retrieval for Fine-Grained Perception in Document Vision-Language Models](https://mlsanity.com/paper/2608.28698/) (2026)
- [Lot Machine: Multimodal Lot Extraction from Auction Catalogs](https://mlsanity.com/paper/2608.30510/) (2026)
- [Transcribe, Then Reason: Two-Pass Decomposition for Multimodal Review](https://mlsanity.com/paper/2609.18958/) (2026)
- [Found but Not Read: When Extracted Text Closes the Retrieval-Reading Gap in Document Vision-Language Models](https://mlsanity.com/paper/2610.02880/) (2026)

## Abstract (arXiv)

Vision-Language Models (VLMs) excel in diverse visual tasks but face challenges in document understanding, which requires fine-grained text processing. While typical visual tasks perform well with low-resolution inputs, reading-intensive applications demand high-resolution, resulting in significant computational overhead. Using OCR-extracted text in VLM prompts partially addresses this issue but underperforms compared to full-resolution counterpart, as it lacks the complete visual context needed for optimal performance. We introduce DocVLM, a method that integrates an OCR-based modality into VLMs to enhance document processing while preserving original weights. Our approach employs an OCR encoder to capture textual content and layout, compressing these into a compact set of learned queries incorporated into the VLM. Comprehensive evaluations across leading VLMs show that DocVLM significantly reduces reliance on high-resolution images for document understanding. In limited-token regimes (448×448), DocVLM with 64 learned queries improves DocVQA results from 56.0% to 86.6% when integrated with InternVL2 and from 84.4% to 91.2% with Qwen2-VL. In LLaVA-OneVision, DocVLM achieves improved results while using 80% less image tokens. The reduced token usage allows processing multiple pages effectively, showing impressive zero-shot results on DUDE and state-of-the-art performance on MP-DocVQA, highlighting DocVLM's potential for applications requiring high-performance and efficiency.