# SynFinTabs: A Dataset of Synthetic Financial Tables for Information and Table Extraction > SynFinTabs is a dataset of 100,000 synthetic financial table images with ground-truth HTML, JSON, CSV, and bounding-box annotations, paired with a LayoutLM fine-tune (FinTabQA) for extractive table question answering. - Authors: Hunter Heidenreich - Published: 2024-12-05 - Updated: 2026-03-30 - Paper: Preprint (arXiv) - Dataset: SynFinTabs Dataset (HuggingFace) - Code: SynFinTabGen (GitHub) - Model: FinTabQA (HuggingFace) - Model: FinTabQA-A4 (HuggingFace) - Source: Bevaya Labs, --- ## TL;DR SynFinTabs is a dataset of 100,000 synthetic financial table images from Queen's University Belfast, generated to resemble tables found in UK Companies House filings and related financial documents. Each image ships with ground-truth HTML, JSON, and CSV representations and bounding-box annotations at the table, row, cell, and word levels. The authors also fine-tune LayoutLM on an extractive table question-answering task to produce FinTabQA, demonstrating the dataset's utility for training information-extraction models. ## What kind of paper is this? **Dominant:** $\Psi\_{\text{Resource}}$: the primary contribution is the dataset itself. The paper's center of gravity is the curation pipeline, the characteristics of the 100,000 synthetic tables, their annotation scheme, and the licensing and public release. The headline claim is a reusable asset for the financial document AI community. **Secondary:** $\Psi\_{\text{Method}}$: the FinTabQA model is a secondary contribution. It demonstrates one practical use of the dataset by fine-tuning LayoutLM for extractive question answering, but the model is not the headline novelty; the dataset is. ## What is the motivation? Table extraction from document images sits at the intersection of computer vision, NLP, and information retrieval, and it remains difficult in part because high-quality labelled data is scarce outside the scientific domain. Most existing table datasets (ICDAR 2013, SciTSR, TableBank, PubTabNet, PubTables-1M) draw heavily from scientific articles because large repositories of such articles are available with source markup that can be used to construct annotations automatically. Financial tables differ from scientific tables in layout, typography, and structural conventions: they tend to be larger, use less dense borders, include currency units, section headings, note columns, and parenthesized negative values, and span a wider range of date formats and font choices. A second gap is the absence of word-level positional ground truth. Many datasets rely on OCR to recover the spatial positions of words after the fact. The authors demonstrate that OCR is unreliable on tabular data: using default EasyOCR parameters on SynFinTabs test images yields approximately 75% exact-match accuracy, compared to near-perfect accuracy when ground-truth positions are used. For training layout language models such as the LayoutLM family, which consume 2D bounding-box coordinates as part of their input, accurate positional annotations are important. Privacy constraints compound the data-scarcity problem. Businesses are often reluctant to share financial documents for model training, particularly when those documents contain sensitive information and training would involve a third-party service provider. A synthetic dataset avoids this constraint by design. ## What is the novelty? SynFinTabs makes two interconnected contributions. First, it introduces a publicly available dataset of 100,000 synthetic financial table images with: - Ground-truth structure in three formats: HTML, JSON, and CSV. - Bounding-box annotations at four levels of granularity: full table, row, cell, and word. Cell bounding boxes represent the full spatially meaningful cell region, not just the minimum pixel region containing text, which is a noted limitation of FinTabNet. - Semantic cell-type labels: "section title", "currency unit", "row header", "column header", and "data". - A predefined question-answer pair per table, with start and end span positions stored explicitly rather than derived from OCR. Second, it releases the dataset generation code (SynFinTabGen), enabling others to extend the pipeline to new domains or larger scales. The generation process proceeds as: specification (structural blueprint) $\rightarrow$ table object (rows, cells, words) $\rightarrow$ HTML document $\rightarrow$ headless browser rendering $\rightarrow$ screenshot image plus bounding-box extraction via the browser DOM. This approach sidesteps OCR entirely for annotation and produces pixel-accurate bounding boxes. **Dataset composition.** The 100,000 tables are spread across six visual themes. Theme 0 (40%) replicates the style of financial statements filed with UK Companies House, designed by inspecting a large sample of filings. The remaining five themes (12% each) represent financial spreadsheet styles and one company-report style (styled annual report). An 80/10/10 train/validation/test split is applied with each theme represented proportionally. Textual cell content uses random English words from a 10,000-word vocabulary; numerical cells contain random numbers. Row headers draw from a list of real financial account names. ## What experiments were performed? The experiments center on extractive table question answering. Each table in SynFinTabs has question-answer pairs for every non-empty cell; one pair per table is designated as the "competition pair" used for training or evaluation depending on the split. Questions take the form "What is the value of [row header] for the year [column header]?" and the target answer is the cell value at that intersection. **FinTabQA training.** LayoutLM base (113 million parameters) is fine-tuned on SynFinTabs using a batch size of 2 and a learning rate found via PyTorch Lightning's Tuner (initial max $3 \times 10^{-5}$). Two model variants are trained: - **FinTabQA**: input is the table image cropped to the table boundary. - **FinTabQA-A4**: input is an A4 page-size image with the table in the top-left corner. This variant is motivated by the observation that when coordinates are rescaled to the 0-1000 "virtual" coordinate space used by LayoutLM, very small or large cropped images suffer more distortion than a consistently-sized A4 canvas. Training uses ground-truth word bounding boxes. Evaluation uses EasyOCR output, with parameters tuned beyond the defaults through a parameter search. The exact-match accuracy metric checks that both the predicted start and end span positions are correct. The F1 score used in standard QA benchmarks is not reported because a partial match in financial cell extraction is considered uninformative: either the full cell value is extracted or it is not. **Real-world evaluation.** A small test set of 50 real-world table images, sampled from UK Companies House filings from March 2023, is manually annotated with two question-answer pairs each, yielding 100 questions. This set tests how well models trained on synthetic data transfer to real documents. **Baseline comparison.** FinTabQA models are compared against GPT-4V under several prompting conditions: question-only, and question plus an instruction specifying that the image contains tabular financial data and requesting parentheses and negation signs be preserved in the response. GPT-4V responses are manually evaluated. **OCR sensitivity analysis.** To separate OCR errors from model errors, both FinTabQA and FinTabQA-A4 are also evaluated on the SynFinTabs test split using the ground-truth words and bounding boxes directly. ## What are the outcomes/conclusions? **SynFinTabs test split.** Using tuned EasyOCR, FinTabQA achieves 95.87% exact-match accuracy and FinTabQA-A4 achieves 94.97% (Table 1). The less-than-one-point difference between the two image-input strategies suggests the choice of image size has limited impact in this setting. **Real-world evaluation.** Results on the 100-question real-world set are reported in Table 2: | Model | Training data | Image size | Prompt | Accuracy | |---|---|---|---|---| | FinTabQA | SynFinTabs | Table boundary | Question | 89% | | FinTabQA-A4 | SynFinTabs | A4 page | Question | 79% | | GPT-4V | Proprietary | Table boundary | Question | 76% | | GPT-4V | Proprietary | Table boundary | Instruction + question | 94% | | GPT-4V | Proprietary | A4 page | Instruction + question | 89% | FinTabQA trained on synthetic data achieves 89% on real-world tables, outperforming GPT-4V with a question-only prompt (76%). When GPT-4V is prompted with an explicit instruction to preserve parentheses and negation signs, it reaches 94%, a gap of 18 percentage points attributed almost entirely to parenthesis handling: 22 of 24 GPT-4V errors with the question-only prompt were caused by the model omitting parentheses around negative values. **OCR impact.** When ground-truth words and bounding boxes are provided, FinTabQA and FinTabQA-A4 achieve 99.98% and 99.99% accuracy respectively, indicating the models themselves are near-perfect on the task. The gap to the OCR-based results (roughly 4-5 percentage points on the synthetic test split) is attributable to OCR errors in two categories: (1) headers not recognized correctly, meaning the question context is corrupted, and (2) answer text not recognized correctly, so the span cannot be located. Using default EasyOCR parameters instead of the tuned parameters reduces accuracy to roughly 75%, a drop of about 20 percentage points. Tesseract, which is commonly used in related work, is reported to perform substantially worse on tabular data. **Limitations.** The paper identifies several explicit constraints. Table content is semantically random: words are drawn from a vocabulary at random and numbers are arbitrary, so models cannot learn to interpret meaning from cell values. All question-answer pairs follow the same grammatical template, which may limit generalization to natural-language paraphrases of the same question. GPT-4V experiments were limited by API cost, restricting the extent of prompt engineering explored. The real-world evaluation set is small (100 questions from 50 tables), making it difficult to draw strong statistical conclusions from the real-world results. ## Reproducibility ### Models FinTabQA and FinTabQA-A4 are both fine-tuned from the base variant of LayoutLM (`microsoft/layoutlm-base-uncased`), which has 113 million parameters. The base LayoutLM is pre-trained for document understanding using text, layout (2D bounding-box coordinates), and image features. The fine-tuned checkpoints are released separately on HuggingFace under the MIT license: [ethanbradley/fintabqa](https://huggingface.co/ethanbradley/fintabqa) (table-boundary variant) and [ethanbradley/fintabqa-a4](https://huggingface.co/ethanbradley/fintabqa-a4) (A4-page variant). ### Algorithms The extractive QA objective follows the standard span prediction setup: the model predicts a start logit and end logit over the context (the flattened list of table words), and the loss is computed against the ground-truth span start and end positions. A post-processing fix is applied at inference: the end position is constrained to come after the predicted start position (argmax over end logits restricted to positions after the start), which yielded approximately 2-point accuracy improvements. Fine-tuning hyperparameters: batch size 2; learning rate selected with PyTorch Lightning Tuner with initial max $3 \times 10^{-5}$; FinTabQA trained for 12 epochs (63 GPU hours); FinTabQA-A4 trained for 11 epochs (57 GPU hours). Best checkpoint selected by lowest validation loss at epoch end. ### Data SynFinTabs comprises 100,000 images split 80/10/10 (train/validation/test). The dataset includes HTML, JSON, and CSV representations of each table; word, cell, row, and table bounding-box annotations; semantic cell-type labels; and pre-computed question-answer pairs with stored span positions. For test-split tables, EasyOCR-extracted words and bounding boxes are also included. The dataset is publicly available on HuggingFace at [ethanbradley/synfintabs](https://huggingface.co/datasets/ethanbradley/synfintabs) under the MIT license. The generation code is available at [ethanbradley/synfintabgen](https://github.com/ethanbradley/synfintabgen) under the MIT license. Textual row headers are drawn from a curated list of real financial account names observed in Companies House filings. Other textual cell content uses random words from a 10,000-word English vocabulary. Numerical cell content is random. No personally identifiable information or actual financial figures from real companies appear in the dataset. ### Evaluation The primary metric is exact-match accuracy requiring both span start and end positions to be correct. Evaluation on SynFinTabs uses the competition pair (one randomly selected question-answer pair per table). For the real-world set, two question-answer pairs per table were manually defined. The real-world evaluation set is small (50 tables, 100 questions) and statistical uncertainty measures are not reported for any result in the paper. ### Hardware All training was performed on a single NVIDIA GRID M60-8Q GPU using the Northern Ireland High Performance Computing (NI-HPC) service. FinTabQA required 63 GPU hours; FinTabQA-A4 required 57 GPU hours. No inference latency or memory figures are reported. ## BibTeX ```bibtex @inproceedings{bradley2026synfintabs, title = {{SynFinTabs}: A Dataset of Synthetic Financial Tables for Information and Table Extraction}, author = {Bradley, Ethan and Roman, Muhammad and Rafferty, Karen and Devereux, Barry}, booktitle = {Document Analysis and Recognition -- ICDAR 2025 Workshops}, publisher = {Springer Nature Switzerland}, address = {Cham}, pages = {85--100}, year = {2026}, month = jan, doi = {10.1007/978-3-032-09371-4_6} } ```