media-ocr-ai

name	media-ocr-ai
description	Modern AI OCR with open-source + commercial-safe models: PaddleOCR (Apache 2.0, Baidu, 80+ languages, layout analysis, tables), EasyOCR (Apache 2.0, JaidedAI, 80+ languages, easiest install), Tesseract 5 (Apache 2.0, mature LSTM backend, 100+ languages), TrOCR (MIT, Microsoft transformer, the only one that really handles cursive handwriting). Extract text from images and PDFs, structured layout (headers/paragraphs/tables), multilingual documents, handwriting, receipts, invoices, screenshots, scanned forms, signage, whiteboards. Use when the user asks to OCR an image, read text from a picture, extract text from a scanned PDF, parse a receipt or invoice, detect table structure, transcribe handwriting, process a multilingual document (English/Japanese/Chinese/Arabic/etc.), handle CJK or RTL scripts, or pick between PaddleOCR vs EasyOCR vs Tesseract vs TrOCR.
argument-hint	[input] [output]

Media OCR AI

Context: $ARGUMENTS

Modern open-source OCR. Goes far beyond plain Tesseract by giving four models for four different jobs: PaddleOCR for layout + tables, EasyOCR for "just read the text", Tesseract for the widest language set, TrOCR for handwriting.

For the in-ffmpeg ocr= filter (Tesseract inside a filtergraph, good for logo detection / rough live text), use ffmpeg-ocr-logo. This skill is for offline, high-accuracy, structured OCR — document processing, not video frame grabs.

Quick start

• Read text from a photo (any language): → Step 3 (ocr.py extract --model easy)
• Structured layout of a PDF page (headers, paragraphs, tables): → Step 4 (ocr.py layout --model paddle)
• Cursive handwriting: → Step 5 (ocr.py handwriting --model trocr)
• Multilingual doc (e.g. English + Japanese): → Step 6 (ocr.py multi-lang --langs en,ja)
• Extract a table as CSV: → Step 7 (ocr.py table --model paddle)
• Pre-install a backend: → Step 2 (ocr.py install <model>)

When to use

• Read text from photos, screenshots, scans, or video frames (single-frame OCR).
• Parse receipts, invoices, business cards, signage — structured documents where layout matters.
• Transcribe handwritten notes, filled forms, or cursive.
• Multilingual content (English mixed with CJK, Arabic, Cyrillic, Thai, etc.).
• PDF text extraction when the PDF is scanned images (use PaddleOCR). For text-native PDFs with embedded font glyphs, reach for pdftotext / pdfplumber first — they're faster and exact.
• Do NOT use for video real-time OCR — go to ffmpeg-ocr-logo for that. Do NOT use for barcode / QR — see ffmpeg-ocr-logo (quirc).

Step 1 — Pick a backend

Model	License	Strengths	Install difficulty	When to pick
PaddleOCR	Apache 2.0	Best layout analysis + table extraction, 80+ languages	medium (paddlepaddle)	Structured documents, receipts, invoices, tables
EasyOCR	Apache 2.0	Easiest install, good general quality, 80+ languages	easy	"Just read this image"
Tesseract 5	Apache 2.0	Widest language support (100+), battle-tested, pure CLI	easiest (system pkg)	Legacy, CPU-only, obscure scripts
TrOCR	MIT	Transformer trained on IAM handwriting; best cursive by far	medium (transformers)	Handwritten notes, cursive, stylized fonts

Pairing advice:

• PaddleOCR has the best layout analysis + table structure. EasyOCR is the easiest to install. Tesseract has the widest language support but worse accuracy on modern content. TrOCR is the only one that really handles cursive handwriting.

Full per-model strengths, install quirks, and language-matrix in references/languages.md.

Step 2 — Install the backend(s)

PEP 723 header in scripts/ocr.py lists all dependencies. Run via uv run once to populate the ephemeral venv. The one-shot installer:

uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py install paddle
uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py install easy
uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py install tesseract
uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py install trocr

Per-backend notes:

• PaddleOCR — pulls paddlepaddle (CPU) or paddlepaddle-gpu (CUDA) via pip. First-use downloads detection + recognition + layout models to ~/.paddleocr/.
• EasyOCR — pulls easyocr + downloads craft-detection + language-specific recognition models to ~/.EasyOCR/ on first use.
• Tesseract 5 — needs the system binary: brew install tesseract tesseract-lang (macOS) / apt install tesseract-ocr tesseract-ocr-all (Debian). pytesseract is just the Python wrapper.
• TrOCR — pulls HuggingFace microsoft/trocr-base-handwritten (or -large-printed) via transformers. Weights cache to ~/.cache/huggingface/hub/.

Step 3 — Extract text (any language, any backend)

uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py extract \
  --model easy --in photo.jpg --out-format text > photo.txt

Output formats:

• --out-format text — raw text, one line per detected region (reading order)
• --out-format json — {"blocks": [{"bbox": [x,y,w,h], "text": ..., "confidence": ...}, ...]}
• --out-format tsv — Tesseract-style TSV (bbox columns + text + conf), good for spreadsheets

--lang sets the detection / recognition language hint (e.g. --lang en or --lang ja). Without it, backends fall back to their default (usually English).

Step 4 — Layout analysis (PaddleOCR only)

Structured layout — headers, paragraphs, lists, tables, figures — is PaddleOCR's flagship capability via PP-StructureV2:

uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py layout \
  --model paddle --in scanned_page.png --out-json layout.json

Output JSON has regions, each with type in {"title", "text", "list", "table", "figure", "figure_caption", "table_caption", "header", "footer", "reference", "equation"}, plus bbox and either text (text regions) or html (table regions, full cell structure).

For a multi-page scanned PDF, loop per page:

# split pages via ImageMagick (media-imagemagick skill)
magick in.pdf page_%03d.png
for p in page_*.png; do
  uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py layout --model paddle --in "$p" --out-json "${p%.png}.json"
done

Step 5 — Handwriting (TrOCR)

uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py handwriting \
  --model trocr --in handwritten.jpg --out note.txt

TrOCR is a line-level recognizer. If the image has multiple lines, pre-segment with PaddleOCR's detection head and then run TrOCR on each line crop (the script's handwriting subcommand does this automatically when --detect paddle is passed).

Variants:

• microsoft/trocr-small-handwritten — fastest
• microsoft/trocr-base-handwritten — default
• microsoft/trocr-large-handwritten — best quality, slower
• microsoft/trocr-*-printed — for printed text (worse on handwriting, so pick only when you know the input is printed)

Select with --variant base-handwritten (or similar).

Step 6 — Multilingual documents

uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py multi-lang \
  --model paddle --langs en,ja,zh --in doc.png --out-json ocr.json

PaddleOCR supports concurrent mixed languages by loading a multilingual recognizer. EasyOCR accepts multiple --languages as a list on init. Tesseract takes -l eng+jpn+chi_sim.

See references/languages.md for the supported-language matrix per backend. Some languages are paddle-only (e.g. Sanskrit, Thai), some are tesseract-only (older scripts, a long tail).

Step 7 — Table extraction as CSV

uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py table \
  --model paddle --in invoice.png --out-csv invoice.csv

Uses PaddleOCR's PP-Structure table engine. Each detected table becomes a <table>-parsed CSV. Multi-table pages produce one CSV per detected table: invoice.csv, invoice_table1.csv, etc.

For complex bordered / borderless tables, PaddleOCR beats the other three backends by a wide margin — only it ships a trained table-structure-recognition model (SLANet).

Available scripts

• scripts/ocr.py — subcommands: extract, layout, handwriting, multi-lang, table, install. Each supports --dry-run, --verbose.

Reference docs

• Read references/languages.md when picking between backends for a specific language, script, or multilingual mix (English + Japanese, CJK, Arabic/Farsi, Thai, Cyrillic).
• Read references/LICENSES.md before shipping OCR output commercially or redistributing the model weights.

Gotchas

• "Tesseract 5 is the default" is wrong. For modern documents (receipts, phone photos, stylized fonts), EasyOCR and PaddleOCR consistently outperform Tesseract. Tesseract wins only on (a) language breadth and (b) CPU-only footprint.
• PaddleOCR install on Apple Silicon needs paddlepaddle (not -gpu), and a specific version. Pin to paddlepaddle>=2.6 in the PEP 723 deps — older 2.4 wheels have M1/M2 build issues.
• EasyOCR downloads ~500 MB per language set on first use. No way around it. Cache persists in ~/.EasyOCR/ — ship the cache with container images to avoid re-downloading.
• TrOCR is line-level, not page-level. Feeding a full page to TrOCR directly produces garbage; you must first segment lines (the handwriting --detect paddle flow does this).
• Tesseract needs per-language .traineddata files. On macOS, brew install tesseract-lang grabs them all; on Linux, apt install tesseract-ocr-<lang> is per-language. Without them, Tesseract silently recognizes in English only and produces garbage for CJK / RTL.
• Right-to-left scripts. Arabic / Hebrew / Farsi reading order is RTL. PaddleOCR and EasyOCR handle it correctly when you pass --lang ar / --lang he. Tesseract often outputs text in visual LTR order — post-process with python-bidi if needed.
• Layout analysis != document OCR. ocr.py layout returns bounding boxes + region types; the text inside each region is still produced by the rec head. If you only want "read the text", skip layout.
• Non-Latin scripts in PaddleOCR need a different rec model. The default en-only model won't recognize Chinese or Japanese. Pass --lang ch / --lang japan (PaddleOCR's codes — see references/languages.md for the full list; they don't all match ISO codes).
• No prompts, no interactive choices. Scripts are non-interactive. All parameters via flags.

Examples

Example 1 — Plain text extraction (EasyOCR, easiest path)

uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py extract \
  --model easy --in receipt.jpg --lang en --out-format text > receipt.txt

Example 2 — Structured receipt parsing with layout + table detection

uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py layout \
  --model paddle --in receipt.jpg --out-json receipt.json
# receipt.json has regions with types {"title","text","table","figure"}
# for the table region, html field contains the cell-accurate markup

Example 3 — Transcribe handwritten notes

uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py handwriting \
  --model trocr --variant base-handwritten \
  --detect paddle --in notebook_page.jpg --out notebook.txt

Example 4 — Japanese menu photo

uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py extract \
  --model paddle --lang japan --in menu.jpg --out-format json > menu.json

Example 5 — Invoice table to CSV

uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py table \
  --model paddle --in invoice.png --out-csv invoice.csv

Example 6 — Batch folder with GNU parallel

Hand off to media-batch:

ls scans/*.png | parallel --jobs 4 \
  uv run ${CLAUDE_SKILL_DIR}/scripts/ocr.py extract \
    --model easy --in {} --out-format text > out/{/.}.txt

Troubleshooting

ImportError: No module named 'paddle' (or paddlepaddle)

Cause: PaddleOCR imported without the backend installed. Solution: pip install paddlepaddle>=2.6 paddleocr (CPU). For CUDA, pip install paddlepaddle-gpu. Or run via uv run which handles the PEP 723 deps automatically.

EasyOCR hangs on first run

Cause: Downloading the craft detector + recognizer models. Can take minutes over slow links. Solution: Wait, or pre-warm with uv run ocr.py install easy --lang en. Once cached in ~/.EasyOCR/, subsequent runs are instant.

Tesseract returns gibberish for Chinese / Japanese / Arabic

Cause: Missing .traineddata. Tesseract defaults to English and silently mis-recognizes. Solution: brew install tesseract-lang (macOS) or apt install tesseract-ocr-jpn tesseract-ocr-chi-sim tesseract-ocr-ara (Debian). Verify: tesseract --list-langs.

TrOCR output is "?????" or random

Cause: Fed a full page to a line-level model, or mismatched variant (printed model on handwriting). Solution: Run with --detect paddle to auto-segment lines, or use a correct variant (-handwritten vs -printed).

cv2.error opening a PDF

Cause: OpenCV doesn't read PDFs directly. Solution: Rasterize first: magick in.pdf -density 300 page_%03d.png (see media-imagemagick), then OCR each PNG.

RTL (Arabic/Hebrew) output is visually reversed

Cause: Some backends output text in visual order (as it appears left-to-right on screen) rather than logical order. Solution: pip install python-bidi && python -c "from bidi.algorithm import get_display; print(get_display(open('out.txt').read()))".