Skill: violet17/image-ocr-local-aipc (version 1.0.0)

--- name: image-ocr-local-AIPC description: > Image OCR, text recognition, extract text from image, scan document, read image text, invoice OCR, receipt OCR, contract recognition, table extraction, business card OCR, ID recognition, screenshot text extraction, document digitization. Runs locally on Windows using the GLM-OCR model, supports mixed Chinese/English text, prioritizes Intel iGPU inference, no cloud API calls. user-invocable: true allowed-tools: Bash(powershell *), Bash(llama-cli *), Read, Write, message --- # Image OCR (Windows · GLM-OCR · llama.cpp Vulkan) **Model**: `ggml-org/GLM-OCR-GGUF` (Q8_0, HuggingFace / hf-mirror) **Inference**: `llama-cli` (llama.cpp Vulkan prebuilt) **SKILL_VERSION**: `v1.0` ## Directory Structure (auto-created or user-specified) ``` <OCR_DIR>\ ← auto-selected drive or user-specified (e.g. C:\image-ocr or D:\image-ocr) ├── llama.cpp\ ← llama-cli.exe and related binaries └── models\ └── GLM-OCR-GGUF\ ├── GLM-OCR-Q8_0.gguf ← main model (~950 MB) └── mmproj-GLM-OCR-Q8_0.gguf ← vision projection layer (~484 MB, required) ``` > **Dependencies**: Model files (GLM-OCR-Q8_0.gguf, mmproj-GLM-OCR-Q8_0.gguf) are downloaded > via Python's `huggingface_hub` (hf download) or `modelscope`. If Python is not installed, > Step 2 will automatically install **Miniforge** (recommended — lightweight, includes conda/pip, > no admin rights required). --- ## ⚠️ AI Assistant Instructions 1. Execute one command at a time; wait for output before proceeding. 2. Stop immediately on error; refer to the Troubleshooting table at the end. 3. Wrap all paths in double quotes. 4. `<OCR_DIR>` is the absolute working directory path, determined after Pre-flight. 5. **Single goal**: Recognize image content and return text results. **Execution flow (do not skip steps)**: ``` Pre-flight: Check working dir + llama.cpp + models → STATUS values Step 1: Install / update llama.cpp (only if MISSING) → LLAMA_OK Step 2: Download models (only if MISSING) → MODEL_OK Step 3: Process recognition result + output → Return result ``` **Progress reporting**: Announce each step before starting, e.g.: `🔍 Pre-flight: Checking environment…` --- ## Pre-flight: Check Environment > 🔍 Pre-flight: Checking working directory, llama.cpp, and model files… ### Locate Working Directory ```powershell # ── Fix encoding for non-ASCII paths (required at the start of every PowerShell script) ── chcp 65001 | Out-Null [Console]::OutputEncoding = [System.Text.Encoding]::UTF8 $OutputEncoding = [System.Text.Encoding]::UTF8 # ── Optional: if you already have a path, fill it in; leave blank to auto-select drive ── $customOcrDir = "" # e.g. "C:\image-ocr" or "D:\image-ocr" # ────────────────────────────────────────────────────────────────────────────────────────── if ($customOcrDir -and (Test-Path (Split-Path $customOcrDir))) { $OCR_DIR = $customOcrDir New-Item -ItemType Directory -Force -Path $OCR_DIR | Out-Null Write-Host "OCR_DIR=$OCR_DIR (user-specified)" } else { $best = Get-PSDrive -PSProvider FileSystem | Where-Object { $_.Free -gt 0 } | Sort-Object Free -Descending | Select-Object -First 1 $OCR_DIR = Join-Path "$($best.Root)" "image-ocr" New-Item -ItemType Directory -Force -Path $OCR_DIR | Out-Null Write-Host "OCR_DIR=$OCR_DIR (auto-selected drive: $($best.Name))" } $env:OCR_DIR = $OCR_DIR ``` **Success criteria**: Output contains a line with `OCR_DIR=`. Record the path and substitute `<OCR_DIR>` in subsequent steps. --- ### Check llama.cpp ```powershell $llamaDir = "<OCR_DIR>\llama.cpp" $cliExe = "$llamaDir\llama-cli.exe" if (Test-Path $cliExe) { $ver = & $cliExe --version 2>&1 if ($ver -match "version:\s*(\d+)") { $build = [int]$Matches[1] if ($build -ge 8400) { Write-Host "OK: llama.cpp build $build >= b8400, skip Step 1" Write-Host "LLAMA_STATUS=READY" } else { Write-Host "WARN: llama.cpp build $build < b8400, upgrade required" Write-Host "LLAMA_STATUS=OUTDATED" } } } else { Write-Host "ERROR: llama-cli.exe not found" Write-Host "LLAMA_STATUS=MISSING" Write-Host " Checked path: $llamaDir" } ``` --- ### Check Model Files ```powershell $modelDir = "<OCR_DIR>\models\GLM-OCR-GGUF" $modelFile = "$modelDir\GLM-OCR-Q8_0.gguf" $mmprojFile = "$modelDir\mmproj-GLM-OCR-Q8_0.gguf" $modelOk = Test-Path $modelFile $mmprojOk = Test-Path $mmprojFile if ($modelOk -and $mmprojOk) { Write-Host "OK: GLM-OCR model files ready, skip Step 2" Write-Host "MODEL_STATUS=READY" } else { if (-not $modelOk) { Write-Host "ERROR: Missing GLM-OCR-Q8_0.gguf" } if (-not $mmprojOk) { Write-Host "ERROR: Missing mmproj-GLM-OCR-Q8_0.gguf" } Write-Host "MODEL_STATUS=MISSING" Write-Host " Checked path: $modelDir" } ``` | Output | Action | |--------|--------| | Both `READY` | ✅ Skip to Step 3 | | `LLAMA_STATUS=MISSING/OUTDATED` | ⬇️ Execute Step 1 | | `MODEL_STATUS=MISSING` | ⬇️ Execute Step 2 | Announce: `✅ Environment check complete. Execute steps as needed.` --- ## Step 1: Install / Update llama.cpp Vulkan > ⬇️ Step 1: Downloading and installing llama.cpp Vulkan… (only when `LLAMA_STATUS=MISSING/OUTDATED`) ```powershell $tag = "b8400" # Replace with the latest tag from https://github.com/ggml-org/llama.cpp/releases/latest $llamaDir = "<OCR_DIR>\llama.cpp" $zip = "$env:TEMP\llama-vulkan.zip" $url = "https://github.com/ggml-org/llama.cpp/releases/download/$tag/llama-$tag-bin-win-vulkan-x64.zip" Write-Host "Downloading llama.cpp $tag ..." Invoke-WebRequest -Uri $url -OutFile $zip New-Item -ItemType Directory -Force -Path $llamaDir | Out-Null Expand-Archive $zip -DestinationPath $llamaDir -Force Remove-Item $zip Write-Host "LLAMA_INSTALL=DONE" ``` | Output | Action | |--------|--------| | `LLAMA_INSTALL=DONE` | ✅ Continue to Step 2 to download models | | Download error | ⛔ Check network, or manually download from browser and extract to `<OCR_DIR>\llama.cpp\` | Announce: `✅ llama.cpp installed. Continue to Step 2 to download models.` --- ## Step 2: Download GLM-OCR Models > 📦 Step 2: Checking Python and downloading GLM-OCR models… (only when `MODEL_STATUS=MISSING`) > **Note**: Models are downloaded via Python's `hf download` (huggingface_hub) or `modelscope`. > The script will auto-locate any existing Python installation; **if none is found, Miniforge will > be installed automatically** to `%USERPROFILE%\miniforge3` (no admin rights required). ### First-time Download Notice (required reading when MODEL_STATUS=MISSING) Announce the following to the user, then ask whether to proceed: ``` 📥 First-time model download is approximately 1.5 GB (GLM-OCR-Q8_0.gguf ~950 MB + mmproj ~484 MB). Estimated download time: • 100 Mbps connection: ~2 minutes • 50 Mbps connection: ~4 minutes • 10 Mbps connection: ~20 minutes Downloads support resumption — if interrupted, re-running this step will automatically continue from where it left off. ✅ Ready — start automatic download 📂 I prefer to download manually — skip automatic download ``` - User chooses **automatic download** → continue with Python check and download commands below - User chooses **manual download** → jump to the "Manual Download Fallback" section at the end of this step --- ### Check Disk Space ```powershell $drive = Split-Path "<OCR_DIR>" -Qualifier $free = (Get-PSDrive ($drive.TrimEnd(':'))).Free / 1GB Write-Host "DISK_FREE=$([math]::Round($free,1))GB" if ($free -lt 2) { Write-Host "DISK_STATUS=LOW" Write-Host "[WARN] Less than 2 GB available — download may fail" } else { Write-Host "DISK_STATUS=OK" } ``` | Output | Action | |--------|--------| | `DISK_STATUS=OK` | ✅ Continue to Python check | | `DISK_STATUS=LOW` | ⚠️ Ask user to free space before continuing | ### Check Python ```powershell # ── Optional: if you know the Python path, fill it in; leave blank to auto-search ── $customPythonExe = "" # e.g. "C:\Python311\python.exe" # ────────────────────────────────────────────────────────────────────────────────── $pythonExe = $null # 1. User-specified path if ($customPythonExe -and (Test-Path $customPythonExe)) { $ver = & $customPythonExe --version 2>&1 Write-Host "OK: Using specified Python: $customPythonExe -> $ver" $pythonExe = $customPythonExe } # 2. Search PATH if (-not $pythonExe) { foreach ($cmd in @("python", "python3", "py")) { if (Get-Command $cmd -ErrorAction SilentlyContinue) { $ver = & $cmd --version 2>&1 Write-Host "OK: Found Python in PATH: $cmd -> $ver" $pythonExe = (Get-Command $cmd).Source break } } } # 3. Scan common install directories if (-not $pythonExe) { $searchPaths = @( "$env:USERPROFILE\miniforge3\python.exe", "$env:USERPROFILE\miniconda3\python.exe", "$env:USERPROFILE\anaconda3\python.exe", "$env:LOCALAPPDATA\Programs\Python\Python3*\python.exe", "C:\Python3*\python.exe" ) foreach ($pattern in $searchPaths) { $found = Get-Item $pattern -ErrorAction SilentlyContinue | Select-Object -First 1 if ($found) { $ver = & $found.FullName --version 2>&1 Write-Host "OK: Found Python in common directory: $($found.FullName) -> $ver" $pythonExe = $found.FullName break } } } if ($pythonExe) { $env:PYTHON_EXE = $pythonExe Write-Host "PYTHON_OK" } else { Write-Host "ERROR: Python not found. Install Miniforge or set `$customPythonExe" Write-Host "PYTHON_MISSING" } ``` **If Python is not found**, install Miniforge: ```powershell $mf = "$env:TEMP\Miniforge3-Windows-x86_64.exe" Invoke-WebRequest ` -Uri "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Windows-x86_64.exe" ` -OutFile $mf Start-Process $mf -ArgumentList "/S /D=$env:USERPROFILE\miniforge3" -Wait Remove-Item $mf $env:PYTHON_EXE = "$env:USERPROFILE\miniforge3\python.exe" & $env:PYTHON_EXE --version Write-Host "PYTHON_OK" ``` ### Download Models **Option A: hf download (recommended)** ```powershell & $env:PYTHON_EXE -m pip install huggingface_hub -q # For users in China: set mirror (skip if outside China) $env:HF_ENDPOINT = "https://hf-mirror.com" $modelDir = "<OCR_DIR>\models\GLM-OCR-GGUF" New-Item -ItemType Directory -Force -Path $modelDir | Out-Null hf download ggml-org/GLM-OCR-GGUF ` --include "GLM-OCR-Q8_0.gguf" "mmproj-GLM-OCR-Q8_0.gguf" ` --local-dir $modelDir Write-Host "MODEL_DOWNLOAD=DONE" ``` **Option B: ModelScope (alternative for users in China)** ```powershell & $env:PYTHON_EXE -m pip install modelscope -q & $env:PYTHON_EXE -c " from modelscope.hub.file_download import model_file_download import os dest = r'<OCR_DIR>\models\GLM-OCR-GGUF' os.makedirs(dest, exist_ok=True) model_file_download('ggml-org/GLM-OCR-GGUF', file_path='GLM-OCR-Q8_0.gguf', local_dir=dest) model_file_download('ggml-org/GLM-OCR-GGUF', file_path='mmproj-GLM-OCR-Q8_0.gguf', local_dir=dest) print('MODEL_DOWNLOAD=DONE') " ``` **Verify:** ```powershell $modelDir = "<OCR_DIR>\models\GLM-OCR-GGUF" Get-Item "$modelDir\GLM-OCR-Q8_0.gguf", "$modelDir\mmproj-GLM-OCR-Q8_0.gguf" | Select-Object Name, @{N='MB';E={[math]::Round($_.Length/1MB,0)}} ``` | Output | Action | |--------|--------| | `MODEL_DOWNLOAD=DONE` | ✅ Continue to Step 3 | | Timeout / repeated failure | ⚠️ Direct user to "Manual Download Fallback" section, or switch between Option A / B and retry | Announce: `✅ Model download complete.` --- ### Manual Download Fallback If automatic download repeatedly fails, guide the user to download manually and place files in the correct directory: ``` ⚠️ Automatic download failed. Please manually download the following two files: 1. GLM-OCR-Q8_0.gguf (~950 MB) HuggingFace: https://huggingface.co/ggml-org/GLM-OCR-GGUF/resolve/main/GLM-OCR-Q8_0.gguf HF Mirror: https://hf-mirror.com/ggml-org/GLM-OCR-GGUF/resolve/main/GLM-OCR-Q8_0.gguf ModelScope: https://modelscope.cn/models/ggml-org/GLM-OCR-GGUF/resolve/master/GLM-OCR-Q8_0.gguf 2. mmproj-GLM-OCR-Q8_0.gguf (~484 MB) HuggingFace: https://huggingface.co/ggml-org/GLM-OCR-GGUF/resolve/main/mmproj-GLM-OCR-Q8_0.gguf HF Mirror: https://hf-mirror.com/ggml-org/GLM-OCR-GGUF/resolve/main/mmproj-GLM-OCR-Q8_0.gguf ModelScope: https://modelscope.cn/models/ggml-org/GLM-OCR-GGUF/resolve/master/mmproj-GLM-OCR-Q8_0.gguf Once downloaded, place both files into: <OCR_DIR>\models\GLM-OCR-GGUF\ Then re-run the Verify command to confirm the files are intact before continuing to Step 3. ``` --- ## Step 3: Process Recognition Result > 🔍 Step 3: Processing GLM-OCR recognition result… ### Determine Input Source | Situation | Action | |-----------|--------| | User message contains a local file path (e.g. `C:\Users\...\xxx.png`) | ⬇️ Case A: extract path from message, call `llama-cli` | | User uploaded an image via the interface; OpenClaw provides a temp path | ⬇️ Case B: retrieve temp path from context, call `llama-cli` | | Neither | ⛔ Ask user to provide a local file path or upload an image | --- ### Case A: User Provides a Local File Path Extract the file path from the user's message, then call `llama-cli` directly: ```powershell # ── Fix encoding ── chcp 65001 | Out-Null [Console]::OutputEncoding = [System.Text.Encoding]::UTF8 $OutputEncoding = [System.Text.Encoding]::UTF8 $imgPath = "<file path extracted from user message>" $m = "<OCR_DIR>\models\GLM-OCR-GGUF\GLM-OCR-Q8_0.gguf" $mm = "<OCR_DIR>\models\GLM-OCR-GGUF\mmproj-GLM-OCR-Q8_0.gguf" if (-not (Test-Path $imgPath)) { Write-Host "ERROR: File not found: $imgPath" exit 1 } $cliExe = "<OCR_DIR>\llama.cpp\llama-cli.exe" $result = & $cliExe ` -m $m ` --mmproj $mm ` --image $imgPath ` -p "Please recognize and extract all text from this image. Output the text content line by line, preserving the original layout." ` -ngl 99 ` --device Vulkan0 ` -c 12000 ` 2>$null Write-Host $result ``` **Success criteria**: stdout contains the recognized text content. --- ### Case B: User Uploaded an Image via the Interface OpenClaw saves uploaded images to a temporary path. Retrieve that path from context and call `llama-cli` the same way: ```powershell # ── Fix encoding ── chcp 65001 | Out-Null [Console]::OutputEncoding = [System.Text.Encoding]::UTF8 $OutputEncoding = [System.Text.Encoding]::UTF8 # imgPath is the temporary image path provided by OpenClaw in context $imgPath = "<temporary image path provided by OpenClaw>" $m = "<OCR_DIR>\models\GLM-OCR-GGUF\GLM-OCR-Q8_0.gguf" $mm = "<OCR_DIR>\models\GLM-OCR-GGUF\mmproj-GLM-OCR-Q8_0.gguf" if (-not (Test-Path $imgPath)) { Write-Host "ERROR: File not found: $imgPath" exit 1 } $cliExe = "<OCR_DIR>\llama.cpp\llama-cli.exe" $result = & $cliExe ` -m $m ` --mmproj $mm ` --image $imgPath ` -p "Please recognize and extract all text from this image. Output the text content line by line, preserving the original layout." ` -ngl 99 ` --device Vulkan0 ` -c 12000 ` 2>$null Write-Host $result ``` **Success criteria**: stdout contains the recognized text content. --- ### Format Output Once the recognized text is obtained, process it according to the user's intent: | Scenario | Handling | |----------|----------| | General text extraction | Output the recognized text as-is, preserving original layout | | Invoice / receipt | Extract structured fields from the text; output as JSON + human-readable format | | Table | Reformat the recognized text as a Markdown table | | Business card | Extract name, title, company, phone, email, address; output as JSON | | ID / certificate | Output structured by original layout | | Screenshot / document | Organize output by paragraph | | User-defined | Process according to the user's stated requirements | **Completion announcement**: ``` ✅ Recognition complete! Let me know if you'd like to re-process, change the output format, or export to a file. ``` | Situation | Handling | |-----------|----------| | `ERROR: File not found` | File path does not exist — ask user to verify the path | | Empty / garbled output | Low image quality — ask user to retake or rescan | | Blurry / low-resolution image | Ask user to retake or zoom in before retrying | | No text detected | Inform user that no recognizable text was found in the image | --- ## Troubleshooting | Error | Cause | Solution | |-------|-------|----------| | `llama-cli` command not found | llama-cli.exe path not set correctly | Verify `<OCR_DIR>\llama.cpp\llama-cli.exe` exists | | `ggml_vulkan: no devices found` | Vulkan driver not installed | Update GPU driver | | `error: unable to open model` | Incorrect model path | Re-run Pre-flight model check to verify path | | `MODEL_DOWNLOAD=` no output | Download interrupted | Switch between Option A / B, or configure proxy | | `PYTHON_MISSING` | Python not installed | Install Miniforge (see Step 2) | | Garbled / blank output | Low image quality | Improve image quality | | VRAM insufficient / crash | Not enough GPU memory | Lower `-ngl` value, or use `--device none` | --- ## References - llama.cpp Releases: https://github.com/ggml-org/llama.cpp/releases - GLM-OCR GGUF: https://huggingface.co/ggml-org/GLM-OCR-GGUF


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)