Surya: Multi-Domain Disambiguation & Applications

• Surya is a multifaceted term denoting an OCR toolkit for printed text, a heliophysics foundation model, several academic authors, and a yoga pose recognition application.
• The term requires explicit disambiguation to avoid conflating methodologies in document AI, solar forecasting, theoretical research, and computer vision.
• Practical insights include detailed performance metrics in OCR evaluations, spatiotemporal forecasting results for heliophysics, and real-time accuracy in yoga pose recognition.

In arXiv usage, Surya is not a single scientific object but a term with several distinct referents. It denotes an open-source, multilingual OCR toolkit for printed text, a 366M parameter foundation model for heliophysics, the surname of multiple authors in deep learning, causal set theory, stochastic processes, combinatorics, mathematical finance, and mathematical relativity, and, in a separate computer-vision context, the Hindu Sun God referenced through Surya Namaskar. The term therefore requires explicit disambiguation at the level of domain, paper, and technical context (Jayatilleke et al., 24 Jul 2025, Roy et al., 18 Aug 2025, Pennington et al., 2018, Dribus, 2013, Sharma et al., 2022).

1. Referential scope in the literature

A recurring source of ambiguity is that identical surface forms correspond to fundamentally different entities.

Referent of “Surya”	Domain	Defining description
Surya OCR	Document AI / OCR	Open-source, multilingual OCR toolkit for printed text
Surya foundation model	Heliophysics / foundation models	366M parameter model trained on SDO observations
Surya Ganguli / Sumati Surya / Budhi Surya	Author surname	Appears in theory papers across multiple fields
Surya in Surya Namaskar	Yoga / computer vision	Religious-cultural referent inside pose-recognition work

This disambiguation matters methodologically. In some papers, Surya names a concrete model family with architecture, training protocol, and benchmark results. In others, it is only an author surname and does not refer to an algorithm, framework, or module. The deep-learning paper “The Emergence of Spectral Universality in Deep Networks” states this explicitly: “Surya” there is Surya Ganguli, not a model named Surya (Pennington et al., 2018).

2. Surya as an OCR and document-processing system

In “Zero-shot OCR Accuracy of Low-Resourced Languages: A Comparative Analysis on Sinhala and Tamil,” Surya is described as an open-source, multilingual OCR toolkit designed to support 90+ languages and to benchmark well against cloud OCR services. Its printed-text OCR stack includes line-level text detection, layout analysis, reading-order detection, table recognition, and LaTeX OCR. Technically, its text detection was trained on four A6000 GPUs for three days using a diverse image set and is built on a modified EfficientViT architecture for segmentation, while its recognition model was trained on the same hardware for two weeks using a modified Donut model with Grouped Query Attention (GQA), a Mixture of Experts (MoE) layer, UTF-16 decoding, and layer-configuration changes. The system is explicitly intended for printed text, not handwriting (Jayatilleke et al., 24 Jul 2025).

The same paper evaluates Surya in a zero-shot OCR setting on Sinhala and Tamil, where “zero-shot” means that the system was used as-is and was not fine-tuned on those languages for the study. The evaluation uses synthetic datasets with clean black text on white backgrounds: 6,969 image-text pairs for Sinhala from sinhala_synthetic_ocr-large, and 7,000 samples for a new synthetic Tamil dataset rendered using six Tamil fonts after filtering OPUS/OpenSubtitles Tamil lines longer than 40 characters. Accuracy is measured with CER, WER, BLEU, ANLS, and METEOR, with CER and WER defined as

$$\text{CER} = \frac{S+I+D}{N}, \qquad \text{WER} = \frac{S+I+D}{N},$$

and ANLS given through thresholded normalized Levenshtein similarity with threshold $T = 0.5$ (Jayatilleke et al., 24 Jul 2025).

On Sinhala, Surya is the best system in the study across all metrics: CER 0.0076, WER 0.0261, BLEU 0.9396, ANLS 0.9920, and METEOR 0.9723. On Tamil, its performance is much weaker: CER 0.1392, WER 0.6500, BLEU 0.1487, ANLS 0.8672, and METEOR 0.3359. The comparison is asymmetric. Surya strongly outperforms Cloud Vision API, Document AI, Tesseract 5.5.0, and Subasa OCR on Sinhala, but is far behind Document AI and Cloud Vision API on Tamil, and is also worse than EasyOCR on Tamil in the core OCR metrics. The paper interprets this as evidence that multilingual zero-shot transfer for low-resource scripts remains highly language-dependent even when scripts are typologically close (Jayatilleke et al., 24 Jul 2025).

A second evaluation, KITAB-Bench, places Surya in a broader Arabic OCR and document-understanding benchmark. There Surya is tested on line detection, line recognition, layout detection, table extraction, OCR/image-to-text, and end-to-end PDF-to-Markdown. The benchmark contains 8,809 samples across 9 major domains and 36 sub-domains. Surya’s profile is mixed. On the full 3,760-image OCR set, it performs very poorly for Arabic recognition, with CER 4.95 and WER 5.61. On line detection, it is substantially stronger, with mAP@50 = 79.67 and [email protected]:0.95 = 27.40; on line recognition the reported values are WER = 1.01 and CER = 0.87. In layout detection it is not the top detector by mAP, but it has relatively strong precision: 0.751 on BCE and 0.782 on DocLayNet. In table extraction it obtains TEDS 50.15 and Jaccard 70.42; for end-to-end PDF-to-Markdown it obtains chrF 58.38, TEDS 44.29, and MARS 51.34 (Heakl et al., 20 Feb 2025).

The Arabic benchmark’s interpretation is not that Surya is uniformly weak, but that it is more competitive in structural tasks than in Arabic text recognition itself. The benchmark attributes Arabic difficulty to cursive script, right-to-left flow, complex typography/calligraphy, font diversity, diacritical marks, numeral recognition, word elongation, and structural issues such as merged cells, tables, and layout complexity. A plausible implication is that Surya’s architecture transfers more effectively to geometric and structural subtasks than to fine-grained script-specific transcription under Arabic document conditions (Heakl et al., 20 Feb 2025).

3. Surya as a heliophysics foundation model

In “Surya: Foundation Model for Heliophysics,” Surya denotes a foundation model for heliophysics rather than an OCR system. It is introduced as a 366M parameter model trained on multi-instrument Solar Dynamics Observatory (SDO) observations, specifically eight Atmospheric Imaging Assembly (AIA) channels and five Helioseismic and Magnetic Imager (HMI) products, for a total of 13 channels per observation. Each observation has the form

$X \in \mathbb{R}^{13 \times 4096 \times 4096},$

and the model uses two timestamps as input, giving

$13 \times 2 \times 4096 \times 4096.$

The processed dataset spans May 13, 2010 to December 31, 2024, with about 257 TB of processed data and a standardized cadence of 12 minutes (Roy et al., 18 Aug 2025).

Architecturally, Surya is a 2-D spatiotemporal transformer with spectral gating blocks, long--short range attention blocks, and a decoder that maps token features back to image space. Tokenization uses patch size $16 \times 16$, hidden dimension $D = 1280$, and 65,536 total tokens, together with Fourier position embeddings. The spectral gating component applies a real FFT, learnable complex-valued frequency weights, and inverse FFT:

$$\widetilde{\mathbf{X}} = \mathcal{F}(\mathbf{X}_s), \qquad \widetilde{\mathbf{X}}' = \widetilde{\mathbf{X}} \odot W_c, \qquad \mathbf{X}_s' = \mathcal{F}^{-1}(\widetilde{\mathbf{X}}'),$$

followed by residual refinement,

$$\mathbf{X}_{\ell+1} = \mathbf{X}_\ell + \mathrm{FFN}\!\left(\mathrm{LN}(\mathbf{X}')\right).$$

Its long--short design combines local windowed attention and efficient global aggregation, reflecting the paper’s claim that solar forecasting requires both fine active-region detail and global coronal context (Roy et al., 18 Aug 2025).

Pretraining is organized around time advancement / forecasting rather than masked reconstruction. The model is given two timestamps 60 minutes apart and trained to predict the SDO state 60 minutes in the future with

$$\mathcal{L}_{\text{MSE}} = \frac{1}{N}\left\lVert \mathbf{X}_{t+1} - f_\theta(\mathbf{X}_t,\mathbf{X}_{t-1}) \right\rVert^2.$$

A second stage performs autoregressive rollout tuning, including

$$\mathcal{L}_{\text{rollout}} = \frac{1}{2N}\Big( \left\lVert \mathbf{X}_{t+1} - f_\theta(\mathbf{X}_t,\mathbf{X}_{t-1}) \right\rVert^2 + \left\lVert \mathbf{X}_{t+2} - f_\theta(f_\theta(\mathbf{X}_t,\mathbf{X}_{t-1}), \mathbf{X}_t) \right\rVert^2 \Big).$$

The rollout schedule is 160,000 steps for one-step training, then 20,000 steps for 2-step ahead, and 4,000 steps each for 3-, 4-, 5-step ahead tuning. The implementation relies on FSDP, mixed precision, and gradient checkpointing because of the memory demands of full-disk $T = 0.5$0 inputs (Roy et al., 18 Aug 2025).

Zero-shot and downstream results position Surya as a general-purpose solar representation learner. The paper reports that rollout tuning improves 12-hour-ahead performance by up to 17.8%, and that the model can be tuned to 24-hour lead times on an 80 GB A100. For downstream tasks, adaptation uses LoRA with

$T = 0.5$1

Reported results include IoU 0.768 and Dice 0.853 for active-region segmentation with only 4.1M trainable parameters; TSS 0.436, HSS 0.522, and F1 0.561 for 24-hour M- or X-class flare forecasting; MSE 0.000126, MAE 0.00451, and MAPE 1.48% for 1343-band solar EUV irradiance prediction; and RMSE 75.92 km/s with MAE 58.06 km/s for 4 days ahead solar wind forecasting. The paper also notes a limitation: because the model is deterministic and trained with MSE, it tends to blur sharp features, even though it may denoise low-SNR channels such as 94 Å and 335 Å (Roy et al., 18 Aug 2025).

4. Surya as author surname in theoretical physics and discrete spacetime

In several papers, Surya is part of an author’s name rather than a model designation. The clearest example is “The Emergence of Spectral Universality in Deep Networks,” where the paper’s content concerns the singular value spectrum of a deep network’s input-output Jacobian at initialization, and “Surya” refers only to Surya Ganguli. The paper studies fully connected networks

$T = 0.5$2

with Jacobian

$T = 0.5$3

and analyzes $T = 0.5$4 using free probability theory and the S-transform. A central scalar is

$T = 0.5$5

which locates the critical line $T = 0.5$6, but the paper emphasizes that criticality alone is not sufficient because the full spectrum may still broaden with depth. Its practical conclusion is that orthogonal weights are necessary for stable limiting distributions, and that ReLU does not preserve the same favorable spectral behavior at large depth as some alternatives such as Hard Tanh, Erf, and SiLU (Pennington et al., 2018).

In mathematical relativity, Surya appears through the Page–Surya–Woolgar positive mass theorem, which motivates the rigidity problem studied in “Conformal boundary rigidity from null geodesic travel times.” That paper asks whether an asymptotically anti-de Sitter spacetime is conformally AdS if every null geodesic from a boundary point returns to the spacetime antipode at conformal infinity. The paper proves affirmative rigidity results in three regimes: under the null energy condition, in the globally static case, and in the globally stationary case. The stationary proof uses the quotient optical manifold, where null geodesics project to magnetic geodesics with Mañé action

$T = 0.5$7

Its main stationary theorem states that if every such null geodesic passes through the antipode, then the spacetime is conformally isometric to anti-de Sitter spacetime (Paternain et al., 27 Jun 2026).

In causal set theory, the paper “On the Axioms of Causal Set Theory” treats Sumati Surya primarily as a major review source for causal set phenomenology and as part of the literature on maximal antichains. It cites Surya’s review Directions in Causal Set Quantum Gravity as a “fine overview” and an “excellent starting point” for topological issues. The paper then argues for revisions to standard causal set axioms, including replacing transitivity by a more primitive causal preorder, replacing interval finiteness by local finiteness, and moving from element-space antichains to relation space, where maximal antichains are claimed to be impermeable. The critique is explicitly framed against the background of ordinary causal set theory and the literature involving Major, Rideout, and Surya on the permeability of maximal antichains (Dribus, 2013).

5. Surya in stochastic processes, combinatorics, and mathematical finance

In probability and stochastic-process theory, Surya is associated with a sequence of papers on mixtures of finite-state Markov jump processes. The 2018 and 2018 follow-up papers summarized in the data treat Surya (2018) as the precursor that extended Frydman-type mixtures to absorbing continuous-time Markov jump processes. Subsequent work generalizes this framework to conditional joint distributions of first exit times, overlapping absorbing sets, explicit Bayesian updates for latent regime probabilities and state probabilities, and reductions to classical phase-type laws when all regimes move at the same speed. These papers emphasize that the observed mixture is non-Markovian, while still admitting explicit formulas for conditional transition matrices, survival functions, densities, Laplace transforms, and moments in terms of intensity matrices and posterior regime weights (Surya, 2018, Surya, 2018).

The later paper “A new class of conditional Markov jump processes with regime switching and path dependence” is explicitly positioned as an extension of Budhi Surya’s earlier work with Frydman. Its main novelty is to reinterpret a finite mixture of Markov jump processes as a distributionally equivalent conditional Markov jump process that reselects the latent speed regime at each jump epoch according to the current state, current time, and path history. This produces explicit formulas for conditional holding times and transition laws, together with a full inference pipeline based on closed-form MLEs, an EM algorithm, Akaike information criterion, an explicit observed Fisher information matrix in a simplified Louis (1982) form, and asymptotic results including consistency, asymptotic normality, and efficiency properties relative to the Cramér–Rao bound (Surya, 2021).

In combinatorics, Surya appears in the phrase Spiro and Surya, whose earlier univariate result on balanced complete $T = 0.5$8-partite graphs is extended in “Deranged Perfect Matchings on complete graph and balanced complete r-partite graph.” The earlier result proved that when $T = 0.5$9 is a uniformly random perfect matching of $X \in \mathbb{R}^{13 \times 4096 \times 4096},$0, the number of edges of $X \in \mathbb{R}^{13 \times 4096 \times 4096},$1 lying in a fixed balanced perfect matching $X \in \mathbb{R}^{13 \times 4096 \times 4096},$2 converges to a Poisson law with mean $X \in \mathbb{R}^{13 \times 4096 \times 4096},$3. The 2025 paper generalizes this to the multivariate setting, obtaining asymptotic independent Poisson limits for vectors of edge-intersection counts in sparse subgraphs (Deng, 20 May 2025).

In mathematical finance, Surya appears through Kyprianou and Surya (2007) in endogenous-bankruptcy models with spectrally negative Lévy asset dynamics. “Optimal Capital Structure with Scale Effects under Spectrally Negative Levy Models” treats that earlier work as its immediate predecessor and generalizes it by allowing state-dependent bankruptcy costs and state-dependent tax benefits. The resulting model preserves threshold-type bankruptcy and uses scale functions $X \in \mathbb{R}^{13 \times 4096 \times 4096},$4, $X \in \mathbb{R}^{13 \times 4096 \times 4096},$5, and $X \in \mathbb{R}^{13 \times 4096 \times 4096},$6 to derive a candidate optimal threshold and a sufficient condition for optimality. The paper’s economic sufficient condition is that $X \in \mathbb{R}^{13 \times 4096 \times 4096},$7 is increasing, $X \in \mathbb{R}^{13 \times 4096 \times 4096},$8 is decreasing, $X \in \mathbb{R}^{13 \times 4096 \times 4096},$9 is increasing, and $13 \times 2 \times 4096 \times 4096.$0, which encodes the stated scale effects (Surya et al., 2011).

6. Surya as the referent of Surya Namaskar in pose-recognition work

In “Surya Namaskar: real-time advanced yoga pose recognition and correction for smart healthcare,” the term appears in its religious-cultural sense. Suryanamaskar, also described as salute to the sun, is a yoga practice devoted to the Hindu Sun God, Surya. The paper proposes a real-time recognition-and-correction system in which a user performs Surya Namaskar in front of a camera, MediaPipe Holistic extracts body landmarks, the landmarks are converted into NumPy arrays, and a deep model built with TensorFlow/Keras classifies the observed sequence into one of 8 Surya Namaskar pose classes: Pranamasana, Hasta Uttanasana, Hasta Padasana, Ashwa-Sanchalanasana, Ashtanga Namaskara, Dandasana, Bhujangasana, and Svanasana (Sharma et al., 2022).

The dataset is manually created by the authors from videos recorded inside a room, with a camera several meters away, at 60 fps, and from front, back, and side views. Although the abstract mentions CNN, the architecture table is a stacked LSTM network with output shapes (None, 10, 64), (None, 10, 128), (None, 64), followed by dense layers of 64, 32, and 8 units, for a total of 596,840 trainable parameters. The paper reports two numerical results: model accuracy of 98.68 percent and accuracy_score(ytrue, yhat) = 0.75. It does not reconcile these values fully, and the data explicitly notes that the reporting is somewhat inconsistent. The intended functionality is a real-time pose recognition and correction assistant, though the paper does not provide a sophisticated rule-based correction engine with joint-angle thresholds (Sharma et al., 2022).

Across these literatures, the principal encyclopedic point is therefore disambiguation. Surya OCR and Surya for heliophysics are model names with concrete architectures, datasets, training objectives, and benchmark suites; Surya Ganguli, Sumati Surya, and Budhi Surya are authors whose names appear in technically unrelated lines of work; and Surya Namaskar preserves the older religious referent of the term. Any technical reading of “Surya” in arXiv materials is incomplete unless the relevant paper is identified first.