# SPRINT: Script-agnostic Structure Recognition in Tables > SPRINT is an image-to-sequence model for table structure recognition that operates on aggressively downsampled images and an OTSL vocabulary to generalize across scripts. The authors also release MUSTARD, a multilingual TSR dataset covering 1,428 tables in 13 languages. - Authors: Hunter Heidenreich - Published: 2026-03-30 - Paper: Preprint (arXiv) - Paper: Published (ICDAR 2024) - Code: SPRINT (code) - Dataset: MUSTARD - Source: Bevaya Labs, --- ## TL;DR SPRINT is an image-to-sequence model for table structure recognition (TSR) that achieves script-agnostic generalization by downsampling table images to 128x128 pixels before encoding, thereby blurring language- and font-specific features. It uses a Global Context Attention (GCA) encoder paired with a transformer decoder trained on the compact Optimized Table Structure Language (OTSL) vocabulary. Alongside the model, the authors release MUSTARD, a multilingual TSR benchmark of 1,428 tables spanning 13 languages, including 11 Indic languages, English, and Chinese. On MUSTARD, SPRINT outperforms the best available comparison model (MTL-TabNet) by an absolute average of 11.12% TEDS-S while also achieving lower inference latency on English benchmarks. ## What kind of paper is this? **Dominant:** $\Psi\_{\text{Method}}$: The paper's primary contribution is the SPRINT architecture and training recipe. It introduces a deliberate design choice (aggressive downsampling to produce a script-agnostic feature representation) and an end-to-end methodology combining SPRINT with a loosely coupled table grid estimator. Ablations over decoder depth, image resolution, and training data mix support this framing. **Secondary:** $\Psi\_{\text{Resource}}$: The MUSTARD dataset is a meaningful standalone contribution: 1,428 annotated multilingual table images across 13 languages, the first such OTSL-labeled multilingual TSR benchmark. Code and dataset are both released under the MIT license. ## What is the motivation? Table structure recognition is a prerequisite for downstream tasks such as information retrieval, table reconstruction, and document understanding. While large English-language TSR benchmarks (PubTabNet, FinTabNet, PubTables-1M) and the models trained on them exist, no labeled multilingual TSR datasets with logical structure annotations were publicly available at the time of this work. Models trained on upsampled English table images implicitly learn language- and font-specific cues that do not transfer to documents in other scripts. The cost of collecting and annotating large labeled corpora for each target language is prohibitive, so the authors seek a model that generalizes across scripts without per-language retraining. A secondary motivation is inference efficiency. Popular image-to-sequence models (MTL-TabNet, TableFormer + HTML) decode large HTML vocabularies, which slows inference relative to what is needed for practical deployment. ## What is the novelty? SPRINT reframes TSR as a script-agnostic cell arrangement prediction problem. The core insight is that two properties together should produce a model that ignores script-specific appearance: first, shrinking the input image to 128x128 pixels converts cell contents into indistinct blobs, removing text-level cues; second, using the minimal OTSL vocabulary (six tokens: `F`, `E`, `L`, `X`, `U`, `N`) rather than a full HTML tag set both speeds decoding and avoids learning vocabulary-linked biases. **OTSL representation.** A table with $R$ rows and $C$ columns is represented as a flat sequence of length $R \times (C+1)$, where `N` acts as a row delimiter appearing at every $(C+1)^{\text{th}}$ position. The remaining tokens encode cell type: - `F` (filled cell with content), `E` (empty cell) - `L` (left-looking: column span), `U` (upward-looking: row span), `X` (cross: both spans) This six-token vocabulary is substantially smaller than an HTML vocabulary (30+ tokens), which reduces decoder logit complexity and shortens sequences. **Architecture.** SPRINT's encoder is a ResNet-31 backbone with Multi-Aspect Global Attention (GCA) interleaved between convolutional layers. The encoder maps a 128x128 image to 512 channels of 16x32 feature maps, which are positionally embedded and fed to a six-layer transformer decoder. The decoder is trained with categorical cross-entropy against ground-truth OTSL sequences, with a maximum sequence length of 224 tokens. **Grid-based alignment.** SPRINT predicts an unconstrained OTSL string, which is then aligned with row and column counts $R$ and $C$ obtained from a pre-trained Table Aware Transformer (TATR) grid estimator. The alignment step pads or trims the predicted string to length $R \times (C+1)$, enforces the periodicity of `N` tokens, and replaces syntactically invalid placements of `L`, `U`, `X`, and `N` with `F`. This produces a well-formed OTSL matrix that can be losslessly converted to HTML using a deterministic algorithm (Algorithm 1 in the paper). **MUSTARD.** The dataset covers 1,214 scanned or printed document tables in 12 languages (11 Indic: Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Oriya, Punjabi, Tamil, Telugu, Urdu; plus Chinese from CTDAR) and 214 scene-text tables in English and Chinese sourced from TabRecSet. Each table is annotated with its OTSL logical structure. The split contains 662 simple tables (no merged cells) and 766 complex tables (at least one merged cell). ## What experiments were performed? The authors train two SPRINT variants: - $\text{SPRINT}\_{\text{FTN}}$: trained on the OTSL-labeled canonical subset of FinTabNet only. - $\text{SPRINT}\_{\text{ALL}}$: trained on a merged dataset combining FinTabNet, PubTabNet, and PubTables-1M canonical subsets. Training runs for more than 80 epochs with a learning rate of 0.0001 on a single NVIDIA RTX A6000 GPU. **Evaluation metric.** The primary metric is TEDS-S (Tree Edit Distance-based Similarity, structure-only), reported for simple tables, complex tables, and overall. Higher is better; scores are in the range [0, 100]. **English benchmarks (canonical OTSL subsets).** $\text{SPRINT}\_{\text{FTN}}$ is evaluated on the FinTabNet canonical test set; $\text{SPRINT}\_{\text{ALL}}$ on PubTabNet and PubTables-1M. Results are compared against the TableFormer + OTSL baseline: | Dataset | TableFormer + OTSL Overall | SPRINT Overall | |---|---|---| | PubTabNet | 95.50 | 97.55 | | FinTabNet | 95.90 | 98.17 | | PubTables-1M | ~97.70 | 97.68 | **English benchmarks (HTML baselines).** On the original PubTabNet and FinTabNet test sets, SPRINT is compared against EDD, GTE, TableFormer, VAST, and MTL-TabNet. SPRINT reaches 95.71% overall TEDS-S on PubTabNet and 98.03% on FinTabNet, approximately 1.5 percentage points below the best-reported scores from MTL-TabNet (97.88% and 98.79% respectively). The authors attribute this gap to MTL-TabNet's use of upscaled images and dual cascaded decoders trained on HTML. **Inference speed.** On a random 400-image FinTabNet subset (50 iterations), SPRINT averages 1.52 seconds per image compared to 2.35 seconds for MTL-TabNet (both measured on the same A6000 GPU) and 3.26 seconds for TableFormer + HTML (a pre-reported figure measured on an AMD EPYC 7763 CPU, not directly comparable). **MUSTARD evaluation.** $\text{SPRINT}\_{\text{FTN}}$ and the FinTabNet-trained MTL-TabNet checkpoint are compared on all 1,428 MUSTARD tables across 14 language-modality groups. The table grid estimator achieves 54.62% exact match for both rows and columns on MUSTARD (versus over 80% on English datasets), reflecting that TATR was pretrained on English data, but the mean L1 error remains below 0.55, which the authors argue limits TEDS-S penalty in practice. **Ablations.** Table 9 in the supplementary material explores combinations of decoder layer count (3, 4, 6, 8) and input image shape (32x32, 32x128, 128x128) across training data conditions. The 6-layer decoder with 128x128 input consistently produces the best or near-best results across all three English benchmarks. ## What are the outcomes/conclusions? On English TSR benchmarks, SPRINT is competitive with methods that use much larger image inputs and vocabularies, approaching but not matching the top HTML-based models. On MUSTARD, SPRINT outperforms MTL-TabNet by an absolute average of 11.12% TEDS-S overall, with gains visible across all 14 language-modality groups (ranging from roughly 2 to 29 percentage points). The largest gains occur in languages where MTL-TabNet performs most poorly (Punjabi: +29.39%, Bengali: +16.94%), which the authors attribute to MTL-TabNet's implicit reliance on English-like visual features. The authors acknowledge several limitations. The table grid estimator (TATR) was pretrained exclusively on English documents, so its row/column count estimates are less accurate on MUSTARD, which could suppress TEDS-S scores. Source code and checkpoints for VAST and the OTSL baseline are not publicly available, so direct replication of those comparisons is not possible. MUSTARD contains roughly 100 tables per language, which is a small evaluation set, and the dataset lacks a train split for non-English languages. The conclusion suggests future work on integrating detected cell bounding boxes with the predicted logical structure to support end-to-end table reconstruction across scripts. ## Reproducibility ### Models SPRINT consists of a ResNet-31 encoder with GCA-based attention (Multi-Aspect Global Attention) and a 6-layer transformer decoder. The encoder produces a 512-channel 16x32 feature tensor; decoder intermediate layers have width 2048. Total parameter count is not reported. Model weights are released via the GitHub repository under the MIT license. The architecture adapts the MASTER scene text recognition framework. ### Algorithms - **Optimizer:** Not explicitly stated; learning rate is 0.0001. - **Training duration:** More than 80 epochs. - **Loss:** Categorical cross-entropy over the OTSL vocabulary (6 characters plus start/stop tokens; vocabulary size of 8). - **Maximum sequence length:** 224 tokens. - **Preprocessing:** Images are resized to 128x128 pixels using standard resize operations; no augmentation details are reported. - **Grid estimator:** TATR v1.1 (pre-trained on FinTabNet, PubTabNet, PubTables-1M); detection threshold 0.25; NMS IOU threshold 0.25 for table-row class. Two checkpoint variants are used: `v1.1-all` for PubTabNet, FinTabNet, and MUSTARD evaluations; `v1.1-PubTables-1m` for PubTables-1M evaluation. - **OTSL-to-HTML conversion:** Deterministic algorithm released with the code. ### Data - **Training data:** OTSL-labeled canonical subsets of PubTabNet (320,000 train images), FinTabNet (88,441 train images), and PubTables-1M (522,874 train images), released by Lysak et al. (ICDAR 2023). - **Validation:** Internal split of PubTabNet train set; non-overlapping PubTabNet validation images used for final reporting. - **MUSTARD:** 1,428 tables used as a held-out evaluation set only; no train split is provided. Tables are sourced from Yojana magazine archives (11 Indic languages), CTDAR documents (102 Chinese tables), and a TabRecSet subset (214 English/Chinese scene tables). The HuggingFace release is at `badrivishalk/MUSTARD` under the MIT license. ### Evaluation - **Primary metric:** TEDS-S, computed from tree edit distance between predicted and ground-truth HTML structure sequences (cell content filtered out). - **Benchmarks:** PubTabNet (original and canonical), FinTabNet (original and canonical), PubTables-1M (canonical), MUSTARD. - **Baselines on English:** EDD, GTE, TableFormer (HTML and OTSL), VAST, MTL-TabNet. VAST and the OTSL baseline did not release code/checkpoints, so comparisons rely on pre-reported numbers. - **Baselines on MUSTARD:** Only MTL-TabNet is compared, as VAST and the OTSL baseline have no public checkpoints. - **No error bars or significance tests are reported.** Results are from single runs. ### Hardware - **Training:** Single NVIDIA RTX A6000 GPU. - **Inference timing:** Single NVIDIA RTX A6000 GPU; 50 iterations over 400 FinTabNet images. SPRINT averages 1.52 seconds per image (1.36s for the SPRINT model itself, 0.16s for post-processing: grid estimation + alignment + HTML conversion). MTL-TabNet was also measured on the A6000 at 2.35 seconds. TableFormer timings (3.26s for HTML, 1.85s for OTSL) are pre-reported figures from the OTSL baseline paper, measured on an AMD EPYC 7763 CPU, not comparable to the A6000 measurements. - **No cost estimates or memory requirements are reported.** - SPRINT's downsampled 128x128 inputs and compact OTSL vocabulary suggest modest inference memory requirements relative to models using full-resolution images. ## BibTeX ```bibtex @inproceedings{kudale2024sprint, title={SPRINT: Script-agnostic Structure Recognition in Tables}, author={Kudale, Dhruv and Kasuba, Badri Vishal and Subramanian, Venkatapathy and Chaudhuri, Parag and Ramakrishnan, Ganesh}, booktitle={Document Analysis and Recognition -- ICDAR 2024 -- 18th International Conference, Athens, Greece, August 30--September 4, 2024, Proceedings, Part V}, pages={350--367}, year={2024}, publisher={Springer}, doi={10.1007/978-3-031-70549-6_21} } ```