{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"hf-model-lightonai--lightonocr-1b-1025","slug":"lightonai--lightonocr-1b-1025","name":"LightOnOCR-1B-1025","type":"model","url":"https://huggingface.co/lightonai/LightOnOCR-1B-1025","page_url":"https://unfragile.ai/lightonai--lightonocr-1b-1025","categories":["data-analysis"],"tags":["transformers","safetensors","mistral3","text-generation","ocr","document-understanding","vision-language","pdf","tables","forms","image-to-text","en","fr","de","es","it","nl","pt","sv","da"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"hf-model-lightonai--lightonocr-1b-1025__cap_0","uri":"capability://image.visual.multilingual.document.ocr.with.vision.language.understanding","name":"multilingual document ocr with vision-language understanding","description":"Processes document images (PDFs, scans, photos) and extracts text with semantic understanding of layout and content structure using a vision-language transformer architecture. The model combines visual feature extraction with language modeling to recognize text across 9 languages (English, French, German, Spanish, Italian, Dutch, Portuguese, Swedish, Danish) while preserving document hierarchy and spatial relationships. Built on Mistral-3 backbone with vision encoder for cross-modal alignment.","intents":["Extract text from scanned documents or PDFs while maintaining reading order and structure","Process multilingual documents in European languages without language-specific model switching","Build document digitization pipelines that understand both text content and visual layout","Create searchable text indices from image-based documents for downstream NLP tasks"],"best_for":["Document processing teams building enterprise digitization workflows","Developers creating multilingual document management systems","Teams processing mixed-language European document collections","Researchers working on document understanding and table extraction"],"limitations":["Model size (1B parameters) may require GPU acceleration for real-time inference; CPU inference latency typically 2-5 seconds per page","No built-in handling of handwritten text — optimized for printed/typed documents","Limited to 9 European languages; no support for Asian scripts, Arabic, or other non-Latin writing systems","Requires sufficient VRAM (minimum 4GB for FP32, 2GB for quantized) for efficient batch processing","No native PDF parsing — requires external PDF-to-image conversion (e.g., pdf2image, PyMuPDF) before inference"],"requires":["Python 3.8+","PyTorch 2.0+ or TensorFlow 2.10+","transformers library 4.30+","Hugging Face Hub access (for model download)","GPU recommended (NVIDIA CUDA 11.8+ or AMD ROCm 5.4+) for production use","4GB+ VRAM for single-image inference, 8GB+ for batch processing"],"input_types":["image (PNG, JPEG, TIFF, WebP)","document pages (scanned PDFs converted to images)","photographs of documents"],"output_types":["text (raw extracted text)","structured data (with layout/spatial metadata if using custom wrapper)","token-level confidence scores (via logits)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-lightonai--lightonocr-1b-1025__cap_1","uri":"capability://data.processing.analysis.table.and.form.structure.extraction.from.document.images","name":"table and form structure extraction from document images","description":"Recognizes and extracts tabular and form data from document images by understanding spatial relationships between cells, rows, and columns through visual feature maps. The vision-language architecture detects structural boundaries and semantic content simultaneously, enabling extraction of structured data (CSV, JSON) from unstructured image input. Preserves cell alignment and hierarchical relationships without requiring explicit table detection preprocessing.","intents":["Extract structured data from scanned invoices, receipts, and financial documents","Convert image-based tables into CSV or JSON format for data pipeline ingestion","Digitize form responses from paper or PDF forms with field-level accuracy","Build automated data entry systems that process document images end-to-end"],"best_for":["Finance and accounting teams processing high-volume document digitization","Data engineering teams building ETL pipelines from document sources","Compliance and legal teams extracting structured data from regulatory documents","Startups building document automation products"],"limitations":["Performance degrades on heavily rotated or skewed documents — requires preprocessing alignment","Complex nested tables or merged cells may produce incomplete or malformed output","No explicit cell boundary detection — relies on implicit spatial understanding which can fail on low-contrast or degraded scans","Requires clean, well-formatted tables; handwritten entries in tables have low accuracy","Output format (raw text vs structured JSON) requires post-processing wrapper — not built-in"],"requires":["Python 3.8+","transformers 4.30+","PIL/Pillow for image preprocessing","Optional: pdf2image or PyMuPDF for PDF input","Optional: custom post-processing script to convert text output to structured formats (CSV, JSON)","GPU with 4GB+ VRAM for batch processing"],"input_types":["image (document page with tables/forms)","scanned PDF pages (converted to images)"],"output_types":["text (raw extracted cell content)","structured data (requires custom wrapper for CSV/JSON output)","spatial metadata (token positions if using logits)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-lightonai--lightonocr-1b-1025__cap_2","uri":"capability://image.visual.cross.lingual.document.text.recognition.with.language.agnostic.visual.encoding","name":"cross-lingual document text recognition with language-agnostic visual encoding","description":"Processes document images in any of 9 supported European languages using a shared visual encoder and language-specific token embeddings, enabling single-model inference without language detection or model switching. The architecture uses language-agnostic visual feature extraction (image → embeddings) followed by language-specific decoding, allowing the same visual understanding to apply across French, German, Spanish, Italian, Dutch, Portuguese, Swedish, and Danish without retraining.","intents":["Process mixed-language document batches without language detection preprocessing","Build document pipelines that handle European language variants transparently","Reduce model serving complexity by eliminating language-specific model routing","Extract text from multilingual forms or documents with consistent quality"],"best_for":["International companies processing documents across European markets","Document management platforms serving multilingual user bases","Teams building language-agnostic document pipelines","Researchers studying cross-lingual OCR and transfer learning"],"limitations":["Limited to 9 European languages — no support for English variants (British, Australian) or non-European languages","Language mixing within single documents may produce degraded output at language boundaries","No explicit language detection — assumes input is one of the 9 supported languages","Performance varies by language; Romance languages (French, Spanish, Italian) typically have higher accuracy than Germanic languages (Swedish, Danish)"],"requires":["Python 3.8+","transformers 4.30+","Hugging Face Hub access","GPU recommended for batch processing"],"input_types":["image (document in any of 9 supported European languages)"],"output_types":["text (extracted in source language)","token-level language tags (if using custom wrapper)"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-lightonai--lightonocr-1b-1025__cap_3","uri":"capability://data.processing.analysis.end.to.end.pdf.document.digitization.with.image.preprocessing","name":"end-to-end pdf document digitization with image preprocessing","description":"Converts PDF documents to searchable text by internally handling page-to-image conversion and OCR inference in sequence. While the model itself processes images, typical deployment patterns include PDF input handling via external libraries (pdf2image, PyMuPDF) integrated into inference pipelines. The model outputs raw text that can be indexed for full-text search or stored with page metadata for document reconstruction.","intents":["Convert scanned PDF archives into searchable text indices","Build document search systems that index PDF content without external OCR services","Create full-text search capabilities for document management systems","Digitize legacy PDF collections for downstream NLP processing"],"best_for":["Document management and archival systems","Enterprise search platforms processing PDF collections","Teams building on-premises document digitization (avoiding cloud OCR costs)","Researchers processing academic papers or technical documentation"],"limitations":["Requires external PDF-to-image conversion library — no native PDF parsing in model","Large PDFs (100+ pages) require batching and memory management; no streaming inference","PDF metadata (bookmarks, annotations) is lost during image conversion","Performance depends on PDF quality — scanned PDFs with low DPI or artifacts produce degraded output","No built-in page numbering or document structure preservation — requires wrapper logic"],"requires":["Python 3.8+","transformers 4.30+","pdf2image or PyMuPDF for PDF-to-image conversion","PIL/Pillow for image handling","GPU with 4GB+ VRAM for batch processing","Optional: elasticsearch or similar for full-text search indexing"],"input_types":["PDF file (scanned or digital)"],"output_types":["text (extracted from all pages)","structured data (page-by-page text with metadata if using custom wrapper)","searchable index (if integrated with search engine)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-lightonai--lightonocr-1b-1025__cap_4","uri":"capability://data.processing.analysis.batch.document.image.processing.with.token.level.confidence.scoring","name":"batch document image processing with token-level confidence scoring","description":"Processes multiple document images in parallel batches while providing token-level confidence scores via transformer logits, enabling quality assessment and selective post-processing. The model outputs raw text tokens with associated probability distributions, allowing downstream systems to flag low-confidence extractions for human review or retry with alternative models. Batch processing amortizes GPU overhead across multiple images for efficient throughput.","intents":["Process document collections with quality metrics for confidence-based filtering","Identify low-confidence extractions for human review in document workflows","Implement adaptive processing pipelines that retry uncertain documents with alternative models","Build quality monitoring dashboards for document digitization operations"],"best_for":["Document processing operations requiring quality assurance","Teams building human-in-the-loop document workflows","Quality engineering teams monitoring OCR accuracy","Production systems needing confidence-based error handling"],"limitations":["Confidence scores are model-calibrated probabilities, not ground-truth accuracy guarantees — miscalibration possible on out-of-distribution documents","Batch processing requires buffering images in memory — large batches (100+ images) may exceed VRAM on consumer GPUs","Token-level scores don't directly map to word or line accuracy — requires aggregation logic","No built-in confidence thresholding or filtering — requires custom post-processing","Confidence scores vary by language; less reliable for low-resource languages in the 9-language set"],"requires":["Python 3.8+","transformers 4.30+","PyTorch or TensorFlow with logits access","GPU with 8GB+ VRAM for large batches","Custom post-processing script to extract and aggregate confidence scores"],"input_types":["batch of images (list of image paths or PIL Image objects)"],"output_types":["text (extracted tokens)","confidence scores (token-level probabilities)","structured data (text + scores if using custom wrapper)"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"hf-model-lightonai--lightonocr-1b-1025__cap_5","uri":"capability://image.visual.vision.language.document.understanding.with.semantic.layout.preservation","name":"vision-language document understanding with semantic layout preservation","description":"Extracts text from documents while implicitly preserving semantic layout information (reading order, paragraph boundaries, section hierarchy) through transformer attention mechanisms that learn spatial relationships between visual regions. Unlike character-level OCR, the model understands document structure holistically, enabling extraction of logically coherent text blocks rather than character sequences. The vision encoder captures spatial features (position, size, proximity) that inform text generation order.","intents":["Extract text from complex documents while maintaining logical reading order","Preserve document structure (sections, paragraphs, lists) without explicit layout detection","Build document understanding systems that capture semantic relationships between text regions","Process documents with mixed content (text, tables, images) with unified model"],"best_for":["Document understanding research and development","Teams building semantic document search systems","Content extraction pipelines requiring structure preservation","Document analysis platforms processing diverse document types"],"limitations":["Layout preservation is implicit and not guaranteed — complex or unusual layouts may produce non-sequential output","No explicit output of layout metadata (bounding boxes, reading order) — requires custom wrapper to extract spatial information","Performance depends on document clarity and contrast — degraded scans may lose layout understanding","Attention mechanisms add computational overhead vs simple character-level OCR","No built-in handling of multi-column layouts or complex nested structures"],"requires":["Python 3.8+","transformers 4.30+","GPU with 4GB+ VRAM","Optional: custom post-processing to extract explicit layout metadata"],"input_types":["image (document page)"],"output_types":["text (with implicit layout preservation)","structured data (with layout metadata if using custom wrapper)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 2.0+ or TensorFlow 2.10+","transformers library 4.30+","Hugging Face Hub access (for model download)","GPU recommended (NVIDIA CUDA 11.8+ or AMD ROCm 5.4+) for production use","4GB+ VRAM for single-image inference, 8GB+ for batch processing","transformers 4.30+","PIL/Pillow for image preprocessing","Optional: pdf2image or PyMuPDF for PDF input","Optional: custom post-processing script to convert text output to structured formats (CSV, JSON)"],"failure_modes":["Model size (1B parameters) may require GPU acceleration for real-time inference; CPU inference latency typically 2-5 seconds per page","No built-in handling of handwritten text — optimized for printed/typed documents","Limited to 9 European languages; no support for Asian scripts, Arabic, or other non-Latin writing systems","Requires sufficient VRAM (minimum 4GB for FP32, 2GB for quantized) for efficient batch processing","No native PDF parsing — requires external PDF-to-image conversion (e.g., pdf2image, PyMuPDF) before inference","Performance degrades on heavily rotated or skewed documents — requires preprocessing alignment","Complex nested tables or merged cells may produce incomplete or malformed output","No explicit cell boundary detection — relies on implicit spatial understanding which can fail on low-contrast or degraded scans","Requires clean, well-formatted tables; handwritten entries in tables have low accuracy","Output format (raw text vs structured JSON) requires post-processing wrapper — not built-in","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.595152063000199,"quality":0.22,"ecosystem":0.5000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.765Z","last_scraped_at":"2026-05-03T14:22:50.443Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":154638,"model_likes":249}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=lightonai--lightonocr-1b-1025","compare_url":"https://unfragile.ai/compare?artifact=lightonai--lightonocr-1b-1025"}},"signature":"DlNp0T21VRi1IUyNZKWWLOiqVl84HqzdKLDJ7oz91yEqKMpXTTnxrN1t/E6RelWwFaixrTdxH/bvN4E3hVBcBA==","signedAt":"2026-06-21T20:13:21.303Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/lightonai--lightonocr-1b-1025","artifact":"https://unfragile.ai/lightonai--lightonocr-1b-1025","verify":"https://unfragile.ai/api/v1/verify?slug=lightonai--lightonocr-1b-1025","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}