--- title: 'dots.mocr-svg: Unified Document Parsing' url: https://www.emergentmind.com/topics/dots-mocr-svg type: topic --- # dots.mocr-svg: Unified Document Parsing dots.mocr-svg is a specialized document parsing paradigm within the Multimodal OCR (MOCR) framework that reconstructs both textual and graphical components of documents by generating unified, code-level outputs, with specific emphasis on faithful, structured SVG (Scalable Vector Graphics) reconstruction. Unlike classical OCR methods, which focus exclusively on text and typically discard or rasterize graphical information, dots.mocr-svg treats visual elements—including charts, diagrams, tables, and icons—as primary targets for parsing, producing SVG code that accurately encodes their geometry and structure. This approach enables end-to-end training over mixed document modalities and allows systems to maintain semantic relationships among elements during parsing, pretraining, and downstream tasks [2603.13032]. ## 1. System Architecture The dots.mocr-svg architecture is composed of a single end-to-end vision-to-text backbone responsible for parsing documents into an interleaved token sequence comprising text and SVG elements [2603.13032, Sec. 3.1]. The principal components are: - **High-Resolution Vision Encoder**: A 1.2B-parameter module capable of ingesting images up to approximately 11 million pixels, preserving fine-grained details across both text and graphics. - **Multimodal Connector**: Projects pixel-level encoder features into the token embedding space used by the downstream decoder. - **Autoregressive LLM Decoder**: Based on Qwen2.5 (1.5B parameters), outputs sequences of triples \((\mathcal B_k,c_k,p_k)\), where \(c_k\) designates a semantic region and \(p_k\) is either a text block or an SVG program. For any detected visual region \((c_k)\), the decoder emits SVG tokens (structurally valid SVG fragments including element types, attributes, and coordinate values), terminated by a delimiter to maintain intermodality alignment. All graphical-to-SVG mapping is performed via direct sequence modeling; there is no separate branching for graphics [2603.13032, Sec. 3.1]. ## 2. Data Processing and Supervision The training corpus for dots.mocr-svg is built by aggregating, cleansing, and annotating graphical and textual data from four primary sources, with a special pipeline for SVG-rich data [2603.13032, Sec. 3.5]: - **Native SVG Asset Collection**: SVGs are sourced from web repositories, design libraries, scientific charting tools, and icon sets. - **Cleaning and Normalization**: SVGO is used to eliminate non-essential metadata, enforce attribute and element normalization, constrain numeric precision, and remove duplications at both code (text hash) and visual (pHash) levels. - **Complexity-Aware Sampling**: SVGs are stratified by token length and path complexity, with sampling balanced across “simple,” “medium,” and “complex” classes to avoid over-representation of trivial assets. - **Supervision Pair Construction**: For each unique, normalized SVG, a high-resolution PNG rendering is paired with the respective SVG code, creating pixel-to-code supervised learning examples. This data engine enables the model to learn direct mappings between document images and code-level semantically rich representations suited for both text and graphical regions. ## 3. Training Strategies dots.mocr-svg employs a three-stage pipeline for model pretraining and specialization [2603.13032, Sec. 3.3]: - **Stage 1: Vision-Language Interface** Generic image-captioning and masked-pixel recovery objectives are used to establish stable visual token embeddings. - **Stage 2: Text-Centric Document Parsing** Large-scale document images derived from PDFs and web sources are introduced, training the model to recognize and layout text, tables, and formulas. Resolution is scaled (from 1k×1k to 4k×4k) to build robustness on dense layouts. - **Stage 3: MOCR-Specific Fine-Tuning** Emphasis is progressively shifted toward document samples with embedded SVG, ultimately composing 20–30% of each minibatch from native SVG–image pairs to reinforce image-to-code alignment. After pretraining, supervised fine-tuning (SFT) is performed on a curated, high-precision dataset, with dots.mocr-svg using a doubled weighting of SVG-rich examples and “harder” SVG-specific loss terms. Two primary model variants are released: - **dots.mocr**: Balanced for general document parsing. - **dots.mocr-svg**: Enhanced for SVG code emission, optimized for high-fidelity graphics parsing. ## 4. SVG Generation and Representation Within the dots.mocr paradigm, all detected graphical regions—including dot/scatter charts, bar charts, scientific diagrams, and schematics—are reconstructed as SVG programs. For statistical charts: - The model generates SVG `` elements for dots, `` for bars, `` or `` for trends, as relevant to the detected chart type. - SVG element attributes (e.g., `cx`, `cy`, `r`, `fill`) are derived from vision encoder outputs through an autoregressive SVG token generation process [2603.13032, Sec. 3.1, 5.2]. For scatter/dot charts specifically, the representation aligns with the agent-based dot extraction pipelines of Socratic Chart [2504.09764], but in dots.mocr-svg the end-to-end model implicitly encodes similar geometric and grouping information within the SVG output stream. ## 5. Evaluation and Quantitative Results Parsing quality is benchmarked on both document-wide and graphics-specific tasks, using the UniSVG ISVGEN metric for render-based comparison with ground-truth SVG [2603.13032, Tab. 4; Sec. 4.1]: - **ISVGEN Score**: Computes the geometric mean of low-level (primitive-level fidelity) and high-level (grouping, semantic structure) metrics via IoU and structural alignment of model-generated and ground-truth SVG renderings. - **Downstream Datasets**: dots.mocr-svg exceeds the performance of OCRVerse and Gemini 3 Pro across all evaluated datasets: - UniSVG overall: 0.902 (vs. 0.763, 0.735) - ChartMimic (statistical charts): 0.905 - Design2Code (UI layouts): 0.834 - GenExam (exam diagrams): 0.800 - SciGen (scientific figures): 0.797 - ChemDraw (chemical diagrams): 0.901 Edit distance and command-level IoU, though common in SVG modeling literature, are supplanted by render-based metrics in this domain to accommodate stylistic normalization and serializer non-uniqueness [2603.13032, Sec. 4.3]. ## 6. Model Outputs and Applications Qualitative outputs (Figs. Example-Icon/Chart/Subject in [2603.13032]) demonstrate the capacity of dots.mocr-svg to reconstruct: - **Icons and UI Graphics**: Raster images parsed into SVG ``, ``, and transform-encoded vector graphics, preserving spatial groupings and stylistic attributes. - **Statistical Charts**: Complete SVG representations including axes, ticks, legends, and discrete data marks (bars, lines, dots) with accurate attribute mapping. - **Technical and Scientific Illustrations**: Circuit diagrams, biological schematics, and multi-level grouped graphics, parallel to structured representations valued in downstream scientific analysis. - **End-to-End Document Reconstruction**: Text and graphics emitted in a unified sequence, supporting document-level semantic parsing and multimodal downstream tasks. ## 7. Relationship to Other Dot Extraction and SVG Pipelines The dots.mocr-svg model is closely related in output and motivation to agent-based pipelines exemplified by Socratic Chart [2504.09764]. Whereas Socratic Chart explicitly constructs dot proposals using parallel generator–critic agents and maps pixel coordinates to SVG space with affine transformations, dots.mocr-svg learns end-to-end SVG generation via transformer-based modeling. Both approaches emphasize robust identification of chart primitives, compatibility with ISVGEN render comparisons, and strict separation of graphical and textual modalities in the output. A plausible implication is that the integration of SVG-level targets in large-scale pretraining not only boosts primitive extraction fidelity in scatter/dot charts but also enables improved semantic alignment for downstream multimodal models operating over complex documents. Source: https://www.emergentmind.com/topics/dots-mocr-svg