[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7C56ATM9VAMAPSWCYZ2FCB0":3,"skill-same:01M7C56ATM9VAMAPSWCYZ2FCB0":45},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":27,"repositoryHtmlUrl":28,"repositoryHomepageUrl":6,"ownerHtmlUrl":29,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":30,"install":31,"resources":44},"01M7C56ATM9VAMAPSWCYZ2FCB0","image-ocr",null,"Extract text content from images using Tesseract OCR via Python","linyouxue\u002Fskill-repair-benchmark","linyouxue","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F216151464?v=4","01M7BJEVCXKD74KH6FG9CBVSQB","林宥学","01M7BJEVE02NP217V93BKZFC0J",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fevaluation\u002Fresults\u002F对于jpg-ocr-stat的尝试\u002Fruns\u002Fjpg-ocr-stat-skillaxe-branches-round23-opus80-20261012-r001-recovery001\u002Finputs\u002Fskills\u002Fimage-ocr\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: image-ocr\ndescription: Extract text content from images using Tesseract OCR via Python\n---\n\n# Image OCR Skill\n\n## Purpose\nThis skill enables accurate text extraction from image files (JPG, PNG, etc.) using Tesseract OCR via the `pytesseract` Python library. It is suitable for scanned documents, screenshots, photos of text, receipts, forms, and other visual content containing text.\n\nFor structured receipt fields, follow **Structured Field Decision Branches** below\nafter the initial extraction. Encode the applicable branches in the extraction\nand update logic before accepting field values; the raw OCR examples are not a\ncomplete field-selection algorithm.\n\n## When to Use\n- Extracting text from scanned documents or photos\n- Reading text from screenshots or image captures\n- Processing batch image files that contain textual information\n- Converting visual documents to machine-readable text\n- Extracting structured data from forms, receipts, or tables in images\n\n## Required Libraries\n\nThe following Python libraries are required:\n\n```python\nimport pytesseract\nfrom PIL import Image\nimport json\nimport os\n```\n\n## Input Requirements\n- **File formats**: JPG, JPEG, PNG, WEBP\n- **Image quality**: Minimum 300 DPI recommended for printed text; clear and legible text\n- **File size**: Under 5MB per image (resize if necessary)\n- **Text language**: Specify if non-English to improve accuracy\n\n## Output Schema\nThe raw OCR helper returns JSON conforming to this schema. For a caller-requested\nspreadsheet, preserve the caller's final schema and keep auxiliary evidence in a\nseparate working file:\n\n```json\n{\n  \"success\": true,\n  \"filename\": \"example.jpg\",\n  \"extracted_text\": \"Full raw text extracted from the image...\",\n  \"confidence\": \"high|medium|low\",\n  \"metadata\": {\n    \"language_detected\": \"en\",\n    \"text_regions\": 3,\n    \"has_tables\": false,\n    \"has_handwriting\": false\n  },\n  \"warnings\": [\n    \"Text partially obscured in bottom-right corner\",\n    \"Low contrast detected in header section\"\n  ]\n}\n```\n\n\n### Field Descriptions\n\n- `success`: Boolean indicating whether text extraction completed\n- `filename`: Original image filename\n- `extracted_text`: Complete text content in reading order (top-to-bottom, left-to-right)\n- `confidence`: Overall OCR confidence level based on image quality and text clarity\n- `metadata.language_detected`: ISO 639-1 language code\n- `metadata.text_regions`: Number of distinct text blocks identified\n- `metadata.has_tables`: Whether tabular data structures were detected\n- `metadata.has_handwriting`: Whether handwritten text was detected\n- `warnings`: Array of quality issues or potential errors\n\n\n## Code Examples\n\n### Basic OCR Extraction\n\n```python\nimport pytesseract\nfrom PIL import Image\n\ndef extract_text_from_image(image_path):\n    \"\"\"Extract text from a single image using Tesseract OCR.\"\"\"\n    img = Image.open(image_path)\n    text = pytesseract.image_to_string(img)\n    return text.strip()\n```\n\n### OCR with Confidence Data\n\n```python\nimport pytesseract\nfrom PIL import Image\n\ndef extract_with_confidence(image_path):\n    \"\"\"Extract text with per-word confidence scores.\"\"\"\n    img = Image.open(image_path)\n\n    # Get detailed OCR data including confidence\n    data = pytesseract.image_to_data(img, output_type=pytesseract.Output.DICT)\n\n    words = []\n    confidences = []\n\n    for i, word in enumerate(data['text']):\n        if word.strip():  # Skip empty strings\n            words.append(word)\n            confidences.append(data['conf'][i])\n\n    # Calculate average confidence\n    avg_confidence = sum(c for c in confidences if c > 0) \u002F len([c for c in confidences if c > 0]) if confidences else 0\n\n    return {\n        'text': ' '.join(words),\n        'average_confidence': avg_confidence,\n        'word_count': len(words)\n    }\n```\n\n### Full OCR with JSON Output\n\n```python\nimport pytesseract\nfrom PIL import Image\nimport json\nimport os\n\ndef ocr_to_json(image_path):\n    \"\"\"Perform OCR and return results as JSON.\"\"\"\n    filename = os.path.basename(image_path)\n    warnings = []\n\n    try:\n        img = Image.open(image_path)\n\n        # Get detailed OCR data\n        data = pytesseract.image_to_data(img, output_type=pytesseract.Output.DICT)\n\n        # Extract text preserving structure\n        text = pytesseract.image_to_string(img)\n\n        # Calculate confidence\n        confidences = [c for c in data['conf'] if c > 0]\n        avg_conf = sum(confidences) \u002F len(confidences) if confidences else 0\n\n        # Determine confidence level\n        if avg_conf >= 80:\n            confidence = \"high\"\n        elif avg_conf >= 50:\n            confidence = \"medium\"\n        else:\n            confidence = \"low\"\n            warnings.append(f\"Low OCR confidence: {avg_conf:.1f}%\")\n\n        # Count text regions (blocks)\n        block_nums = set(data['block_num'])\n        text_regions = len([b for b in block_nums if b > 0])\n\n        result = {\n            \"success\": True,\n            \"filename\": filename,\n            \"extracted_text\": text.strip(),\n            \"confidence\": confidence,\n            \"metadata\": {\n                \"language_detected\": \"en\",\n                \"text_regions\": text_regions,\n                \"has_tables\": False,\n                \"has_handwriting\": False\n            },\n            \"warnings\": warnings\n        }\n\n    except Exception as e:\n        result = {\n            \"success\": False,\n            \"filename\": filename,\n            \"extracted_text\": \"\",\n            \"confidence\": \"low\",\n            \"metadata\": {\n                \"language_detected\": \"unknown\",\n                \"text_regions\": 0,\n                \"has_tables\": False,\n                \"has_handwriting\": False\n            },\n            \"warnings\": [f\"OCR failed: {str(e)}\"]\n        }\n\n    return result\n\n# Usage\nresult = ocr_to_json(\"document.jpg\")\nprint(json.dumps(result, indent=2))\n```\n\n### Batch Processing Multiple Images\n\n```python\nimport pytesseract\nfrom PIL import Image\nimport json\nimport os\nfrom pathlib import Path\n\ndef process_image_directory(directory_path, output_file):\n    \"\"\"Process all images in a directory and save results.\"\"\"\n    image_extensions = {'.jpg', '.jpeg', '.png', '.webp'}\n    results = []\n\n    for file_path in sorted(Path(directory_path).iterdir()):\n        if file_path.suffix.lower() in image_extensions:\n            result = ocr_to_json(str(file_path))\n            results.append(result)\n            print(f\"Processed: {file_path.name}\")\n\n    # Save results\n    with open(output_file, 'w') as f:\n        json.dump(results, f, indent=2)\n\n    return results\n```\n\n\n## Tesseract Configuration Options\n\n### Language Selection\n\n```python\n# Specify language (default is English)\ntext = pytesseract.image_to_string(img, lang='eng')\n\n# Multiple languages\ntext = pytesseract.image_to_string(img, lang='eng+fra+deu')\n```\n\n### Page Segmentation Modes (PSM)\n\nUse `--psm` to control how Tesseract segments the image:\n\n```python\n# PSM 3: Fully automatic page segmentation (default)\ntext = pytesseract.image_to_string(img, config='--psm 3')\n\n# PSM 4: Assume single column of text\ntext = pytesseract.image_to_string(img, config='--psm 4')\n\n# PSM 6: Assume uniform block of text\ntext = pytesseract.image_to_string(img, config='--psm 6')\n\n# PSM 11: Sparse text - find as much text as possible\ntext = pytesseract.image_to_string(img, config='--psm 11')\n```\n\nCommon PSM values:\n- `0`: Orientation and script detection (OSD) only\n- `3`: Fully automatic page segmentation (default)\n- `4`: Single column of text of variable sizes\n- `6`: Uniform block of text\n- `7`: Single text line\n- `11`: Sparse text\n- `13`: Raw line\n\n\n## Image Preprocessing\n\nFor better OCR accuracy, preprocess images:\n\n```python\nfrom PIL import Image, ImageFilter, ImageOps\n\ndef preprocess_image(image_path):\n    \"\"\"Preprocess image for better OCR results.\"\"\"\n    img = Image.open(image_path)\n\n    # Convert to grayscale\n    img = img.convert('L')\n\n    # Increase contrast\n    img = ImageOps.autocontrast(img)\n\n    # Apply slight sharpening\n    img = img.filter(ImageFilter.SHARPEN)\n\n    return img\n\n# Use preprocessed image for OCR\nimg = preprocess_image(\"document.jpg\")\ntext = pytesseract.image_to_string(img)\n```\n\n### Advanced Preprocessing Strategies\n\nFor difficult images (low contrast, faded text, dark backgrounds), try multiple preprocessing approaches:\n\n1. **Grayscale + Autocontrast** - Basic enhancement for most images\n2. **Inverted** - Use `ImageOps.invert()` for dark backgrounds with light text\n3. **Scaling** - Upscale small images (e.g., 2x or 3x) before OCR to improve character recognition. For faint or thin fonts on receipts, 3x with autocontrast frequently recovers decimal points and punctuation that 1x\u002F2x lose.\n4. **Thresholding** - Convert to binary using `img.point(lambda p: 255 if p > threshold else 0)` with different threshold values (e.g., 100, 128)\n5. **Sharpening** - Apply `ImageFilter.SHARPEN` to improve edge clarity\n\n\n## Multi-Pass OCR Strategy\n\nFor challenging images, a single OCR pass may miss text. Use multiple passes with different configurations:\n\n1. **Try multiple PSM modes** - Different page segmentation modes work better for different layouts (e.g., `--psm 6` for blocks, `--psm 4` for columns, `--psm 11` for sparse text)\n\n2. **Try multiple preprocessing variants** - Run OCR on several preprocessed versions of the same image\n\n3. **Retain results** - Keep each pass's text and source separately for structured-field decisions. Concatenation can improve raw-text coverage, but it loses the evidence needed to select one field value.\n\n```python\ndef multi_pass_ocr(image_path):\n    \"\"\"Run OCR with multiple strategies and combine results.\"\"\"\n    img = Image.open(image_path)\n    gray = ImageOps.grayscale(img)\n    w, h = img.size\n\n    # Generate preprocessing variants (include an upscaled variant; 3x often\n    # recovers thin punctuation such as the decimal point in prices)\n    variants = [\n        ImageOps.autocontrast(gray),\n        ImageOps.invert(ImageOps.autocontrast(gray)),\n        gray.filter(ImageFilter.SHARPEN),\n        ImageOps.autocontrast(img.resize((w * 3, h * 3), Image.LANCZOS).convert('L')),\n    ]\n\n    # PSM modes to try\n    psm_modes = ['--psm 6', '--psm 4', '--psm 11']\n\n    all_text = []\n    for variant in variants:\n        for psm in psm_modes:\n            try:\n                text = pytesseract.image_to_string(variant, config=psm)\n                if text.strip():\n                    all_text.append(text)\n            except Exception:\n                pass\n\n    # Combine all extracted text\n    return \"\\n\".join(all_text)\n```\n\nThis approach improves extraction for receipts, faded documents, and images with varying quality.\n\n### Structured Field Decision Branches\n\nAfter one batch extraction, save provisional results and a separate evidence\nrecord containing each candidate's raw text, field label, image region, and OCR\nsource. Evaluate the following triggers for every field, including non-empty and\nformat-valid fields. Select a branch before selecting a value. A clear field can\nbe confirmed directly; OCR frequency, keyword rank, and variant order alone are\nnot confirmation evidence.\n\n#### Branch A — Uncertain printed date digit\n\n**Trigger:** the date's day, month, or year has faint\u002Fbroken strokes, loop-like\nmarks, two arcs with unclear closure, a digit touching a separator, or conflicting\nreadings. This branch also applies when all OCR variants agree but the printed\ncharacter remains uncertain. A calendar-valid result does not clear this trigger.\n\n**Execute:**\n1. Crop the complete date line with its label, separators, and neighboring date\n   components, retaining margins around the uncertain character. Use the original\n   image as well as an enlarged grayscale copy; thresholding can erase a gap or\n   join two strokes.\n2. View the saved crop with the available image-view tool (for example, the file\n   editor's image view). For a possible open-arc\u002Fclosed-loop confusion such as\n   `3` versus `8`, examine the left-side gaps and closures in the original pixels.\n   Compare nearby printed digits in the same font; ignore handwriting and OCR\n   frequency as substitutes for that comparison. Use line OCR on the crop for\n   additional readings, keeping them as alternatives rather than votes.\n3. Resolve the printed character and the day\u002Fmonth\u002Fyear order from the same date\n   field and receipt context. Record the crop and visual reason, then convert to\n   ISO. If the pixels remain inconclusive, keep the field pending instead of\n   replacing one legal month with another by a global substitution rule.\n\n**Confirm when:** the selected date components have identifiable source evidence,\nnot merely a valid format or more OCR occurrences. When no visual tool is\navailable, use focused OCR with retained context and acknowledge unresolved\nambiguity rather than claiming visual verification.\n\n#### Branch B — Inclusive total, rounding, and a later final total\n\n**Trigger:** the payment block contains a pre-round total (possibly labeled as\ntax-inclusive), a signed rounding adjustment, and a later `TOTAL`, `NET TOTAL`,\n`NETT TOTAL`, or other final-payable line. Different amounts in these roles are\nnot conflicting OCR readings of one field.\n\n**Execute:**\n1. Crop and read the whole payment block together. Assign candidates to separate\n   roles: subtotal, tax\u002Fservice components, pre-round payable, signed adjustment,\n   final payable, tendered cash, and change. Keep a tax-summary table outside this\n   payment block; its own `TOTAL` is not the amount due.\n2. Preserve the `+` or `-` on the adjustment. Parse these monetary tokens with\n   `Decimal` and check `final_payable == pre_round_payable + signed_adjustment`.\n   The sign is part of the value, not punctuation to discard. If the pre-round\n   payable already includes tax\u002Fservice charges, use those components only to\n   cross-check it, not to add them again.\n3. Select the explicitly printed final-payable amount when the field association\n   and arithmetic agree, even if an earlier tax-inclusive amount occurs in more\n   OCR passes or has a higher-ranked keyword. The final label can be larger or\n   bolder than surrounding text; inspect that line separately if OCR misses it.\n4. If a digit or separator is missing or the arithmetic disagrees, review the\n   corresponding local crop. Accept the result only after resolving the reading;\n   do not invent a rounding adjustment or silently force the arithmetic to fit.\n\n**Confirm when:** the final amount is anchored to its own label and verified\nagainst the printed settlement relation. If no rounding\u002Ffinal-total pattern is\npresent, resolve the final-payable role from the receipt's actual labels; do not\napply this branch's adjustment speculatively. Keywords such as `GST` identify a\npossible component, not an automatic reason to discard an inclusive payable.\n\n#### Branch C — Amount split across lines or repaired digits\n\n**Trigger:** an amount keyword has no number on its line, or a candidate relies\non inserting a decimal, removing an OCR character, or joining separated digits.\n\n**Execute:** crop the label and neighboring lines together. Locate the numeric\nregion that is visually associated with that label; exclude quantity, date,\nidentifier, tax-rate, and change fields. Use next-line\u002Ftrailing tokens only as\ncandidates. Support each punctuation\u002Fdigit repair from the original monetary\nregion, and then apply Branch B if settlement roles are present.\n\n**Confirm when:** both the label association and repaired number are supported.\nNeither the last number on a line nor successful parsing clears this branch.\n\n#### Branch D — Reusing confirmed fields after a parser change\n\n**Trigger:** changing shared parsing\u002Fselection logic, adding a raw-OCR preference,\nor retrying extraction after any fields have been confirmed.\n\n**Execute:** save and reload the confirmed field values with their evidence before\nthe change. Run the new logic into provisional candidates. Merge only reviewed\nupdates to unresolved or demonstrably affected fields; retain every other\nconfirmed field. A changed candidate requires stronger same-field evidence and\na recorded reason before replacing a confirmed value. Rebuild the deliverable\nfrom this merged accepted state, not from fresh unreviewed batch output.\n\n**Confirm when:** a before\u002Fafter field comparison shows that every replacement\nhas been reviewed. Execute the write, reopen the destination workbook, and compare\nits stored values with the accepted state. Logs or values left only in memory do\nnot demonstrate delivery. Reserve budget for this final readback instead of\nunnecessarily repeating full-batch OCR at the end.\n\n### Numeric Normalization for Receipts and Forms\n\nOCR frequently corrupts decimal separators and digit spacing in monetary amounts.\nApply the following repairs only inside a monetary region associated with its\nlabel, and only when its printed evidence supports the repair (Branch C):\n\n- Replace `:` between digits with `.` (e.g., `92:80` -> `92.80`).\n- Collapse a single space between a 1-4 digit group and a 2-digit group into `.` (e.g., `92 80` -> `92.80`, `1 234 56` -> keep the comma-thousands intact but treat trailing ` 56` as decimals only when no other decimal separator is present).\n- Reinterpret bare digit clusters of length >=3 whose last two digits look like cents as `X.YY` when no decimal separator was recognized (e.g., `9280` -> `92.80`) **only** when the line contains a strong keyword such as `GRAND TOTAL` and no better candidate exists.\n- Remove clearly extraneous surrounding OCR marks; retain decimal\u002Fthousands punctuation and any genuine sign, especially on rounding adjustments.\n- Accept commas as thousands separators: `1,234.56` -> `1234.56`.\n\nPrefer a well-formed monetary token in the identified field. Next-line candidates\nand decimal repairs must pass Branch C; this helper parses a token but does not\ndecide whether it is the final amount. Keep date and ID parsing separate.\n\n```python\nimport re\nfrom decimal import Decimal\n\nMONEY_STRICT = re.compile(r'[+-]?(?:\\d{1,3}(?:,\\d{3})+|\\d+)\\.\\d{2}')\n\ndef parse_money(token: str, *, decimal_repair_supported=False):\n    \"\"\"Parse a field-associated monetary token, preserving a printed sign.\"\"\"\n    if token is None:\n        return None\n    s = token.strip().replace('\\u2212', '-')\n    if MONEY_STRICT.fullmatch(s):\n        return Decimal(s.replace(',', ''))\n    if not decimal_repair_supported:\n        return None\n    # Enable only after the original region establishes the decimal position.\n    candidates = [\n        re.sub(r'(\\d):(\\d{2})$', r'\\1.\\2', s),\n        re.sub(r'(\\d)\\s+(\\d{2})$', r'\\1.\\2', s),\n    ]\n    if re.fullmatch(r'[+-]?\\d{3,}', s):\n        candidates.append(s[:-2] + '.' + s[-2:])\n    for candidate in candidates:\n        if MONEY_STRICT.fullmatch(candidate):\n            return Decimal(candidate.replace(',', ''))\n    return None\n```\n\n\n## Error Handling\n\n### Common Issues and Solutions\n\n**Issue**: Tesseract not found\n\n```python\n# Verify Tesseract is installed\ntry:\n    pytesseract.get_tesseract_version()\nexcept pytesseract.TesseractNotFoundError:\n    print(\"Tesseract is not installed or not in PATH\")\n```\n\n**Issue**: Poor OCR quality\n\n- Preprocess image (grayscale, contrast, sharpen)\n- Use appropriate PSM mode for the document type\n- Ensure image resolution is sufficient (300+ DPI)\n\n**Issue**: Empty or garbage output\n\n- Check if image contains actual text\n- Try different PSM modes\n- Verify image is not corrupted\n\n**Issue**: Keyword detected but no numeric value parsed\n\n- Do not immediately return null. First:\n  1. Run Branch C on the label and its neighboring numeric region.\n  2. If multiple settlement amounts or rounding appear, run Branch B.\n  3. Retain unresolved candidates and continue focused review within the available budget.\n- If the field remains unresolved, report extraction failure instead of accepting\n  an unrelated but parseable number. Non-empty ambiguous fields use the same\n  decision branches; review is not limited to null values.\n\n\n## Quality Self-Check\n\nBefore returning results, verify:\n\n- [ ] Output is valid JSON (use `json.loads()` to validate)\n- [ ] All required fields are present (`success`, `filename`, `extracted_text`, `confidence`, `metadata`)\n- [ ] Text preserves logical reading order\n- [ ] Confidence level reflects actual OCR quality\n- [ ] Warnings array includes all detected issues\n- [ ] Special characters are properly escaped in JSON\n\n### Structured-Value Extraction Self-Check\n\nWhen the caller extracts specific fields (amounts, dates, IDs), also verify:\n\n- [ ] For every image where a target keyword (e.g., `GRAND TOTAL`, `TOTAL RM`) is detected in **any** OCR variant, a candidate value was produced, or the reason for failure is explicitly logged.\n- [ ] Uncertain printed date components passed Branch A, including legal dates with weak source readings and correlated OCR agreement.\n- [ ] Settlement totals passed Branch B where applicable; pre-round, rounded, tax-summary, tendered, and change amounts were kept in separate roles.\n- [ ] Decimal repairs and split-line candidates passed Branch C, preserving a genuine adjustment sign.\n- [ ] Parser changes and retries passed Branch D; confirmed fields were preserved unless an evidenced correction was accepted.\n- [ ] The executed script produced the requested file, and readback matched the accepted fields and caller's schema, coverage, and ordering.\n\n\n## Limitations\n\n- Tesseract works best with printed text; handwriting recognition is limited\n- Accuracy decreases with decorative fonts, artistic text, or extreme stylization\n- Mathematical equations and special notation may not extract accurately\n- Redacted or watermarked text cannot be recovered\n- Severe image degradation (blur, noise, low resolution) reduces accuracy\n- Complex multi-column layouts may require custom PSM configuration\n- Thin punctuation (decimal points, colons) is often lost or substituted; always attempt numeric normalization before rejecting a value\n\n\n## Version History\n\n- **1.0.0** (2026-01-13): Initial release with Tesseract\u002Fpytesseract OCR\n- **1.1.0**: Added value-level voting across variants, numeric normalization guidance for OCR-corrupted amounts, and structured-value extraction self-check.\n","# Image OCR Skill\n\n## Purpose\nThis skill enables accurate text extraction from image files (JPG, PNG, etc.) using Tesseract OCR via the `pytesseract` Python library. It is suitable for scanned documents, screenshots, photos of text, receipts, forms, and other visual content containing text.\n\nFor structured receipt fields, follow **Structured Field Decision Branches** below\nafter the initial extraction. Encode the applicable branches in the extraction\nand update logic before accepting field values; the raw OCR examples are not a\ncomplete field-selection algorithm.\n\n## When to Use\n- Extracting text from scanned documents or photos\n- Reading text from screenshots or image captures\n- Processing batch image files that contain textual information\n- Converting visual documents to machine-readable text\n- Extracting structured data from forms, receipts, or tables in images\n\n## Required Libraries\n\nThe following Python libraries are required:\n\n```python\nimport pytesseract\nfrom PIL import Image\nimport json\nimport os\n```\n\n## Input Requirements\n- **File formats**: JPG, JPEG, PNG, WEBP\n- **Image quality**: Minimum 300 DPI recommended for printed text; clear and legible text\n- **File size**: Under 5MB per image (resize if necessary)\n- **Text language**: Specify if non-English to improve accuracy\n\n## Output Schema\nThe raw OCR helper returns JSON conforming to this schema. For a caller-requested\nspreadsheet, preserve the caller's final schema and keep auxiliary evidence in a\nseparate working file:\n\n```json\n{\n  \"success\": true,\n  \"filename\": \"example.jpg\",\n  \"extracted_text\": \"Full raw text extracted from the image...\",\n  \"confidence\": \"high|medium|low\",\n  \"metadata\": {\n    \"language_detected\": \"en\",\n    \"text_regions\": 3,\n    \"has_tables\": false,\n    \"has_handwriting\": false\n  },\n  \"warnings\": [\n    \"Text partially obscured in bottom-right corner\",\n    \"Low contrast detected in header section\"\n  ]\n}\n```\n\n\n### Field Descriptions\n\n- `success`: Boolean indicating whether text extraction completed\n- `filename`: Original image filename\n- `extracted_text`: Complete text content in reading order (top-to-bottom, left-to-right)\n- `confidence`: Overall OCR confidence level based on image quality and text clarity\n- `metadata.language_detected`: ISO 639-1 language code\n- `metadata.text_regions`: Number of distinct text blocks identified\n- `metadata.has_tables`: Whether tabular data structures were detected\n- `metadata.has_handwriting`: Whether handwritten text was detected\n- `warnings`: Array of quality issues or potential errors\n\n\n## Code Examples\n\n### Basic OCR Extraction\n\n```python\nimport pytesseract\nfrom PIL import Image\n\ndef extract_text_from_image(image_path):\n    \"\"\"Extract text from a single image using Tesseract OCR.\"\"\"\n    img = Image.open(image_path)\n    text = pytesseract.image_to_string(img)\n    return text.strip()\n```\n\n### OCR with Confidence Data\n\n```python\nimport pytesseract\nfrom PIL import Image\n\ndef extract_with_confidence(image_path):\n    \"\"\"Extract text with per-word confidence scores.\"\"\"\n    img = Image.open(image_path)\n\n    # Get detailed OCR data including confidence\n    data = pytesseract.image_to_data(img, output_type=pytesseract.Output.DICT)\n\n    words = []\n    confidences = []\n\n    for i, word in enumerate(data['text']):\n        if word.strip():  # Skip empty strings\n            words.append(word)\n            confidences.append(data['conf'][i])\n\n    # Calculate average confidence\n    avg_confidence = sum(c for c in confidences if c > 0) \u002F len([c for c in confidences if c > 0]) if confidences else 0\n\n    return {\n        'text': ' '.join(words),\n        'average_confidence': avg_confidence,\n        'word_count': len(words)\n    }\n```\n\n### Full OCR with JSON Output\n\n```python\nimport pytesseract\nfrom PIL import Image\nimport json\nimport os\n\ndef ocr_to_json(image_path):\n    \"\"\"Perform OCR and return results as JSON.\"\"\"\n    filename = os.path.basename(image_path)\n    warnings = []\n\n    try:\n        img = Image.open(image_path)\n\n        # Get detailed OCR data\n        data = pytesseract.image_to_data(img, output_type=pytesseract.Output.DICT)\n\n        # Extract text preserving structure\n        text = pytesseract.image_to_string(img)\n\n        # Calculate confidence\n        confidences = [c for c in data['conf'] if c > 0]\n        avg_conf = sum(confidences) \u002F len(confidences) if confidences else 0\n\n        # Determine confidence level\n        if avg_conf >= 80:\n            confidence = \"high\"\n        elif avg_conf >= 50:\n            confidence = \"medium\"\n        else:\n            confidence = \"low\"\n            warnings.append(f\"Low OCR confidence: {avg_conf:.1f}%\")\n\n        # Count text regions (blocks)\n        block_nums = set(data['block_num'])\n        text_regions = len([b for b in block_nums if b > 0])\n\n        result = {\n            \"success\": True,\n            \"filename\": filename,\n            \"extracted_text\": text.strip(),\n            \"confidence\": confidence,\n            \"metadata\": {\n                \"language_detected\": \"en\",\n                \"text_regions\": text_regions,\n                \"has_tables\": False,\n                \"has_handwriting\": False\n            },\n            \"warnings\": warnings\n        }\n\n    except Exception as e:\n        result = {\n            \"success\": False,\n            \"filename\": filename,\n            \"extracted_text\": \"\",\n            \"confidence\": \"low\",\n            \"metadata\": {\n                \"language_detected\": \"unknown\",\n                \"text_regions\": 0,\n                \"has_tables\": False,\n                \"has_handwriting\": False\n            },\n            \"warnings\": [f\"OCR failed: {str(e)}\"]\n        }\n\n    return result\n\n# Usage\nresult = ocr_to_json(\"document.jpg\")\nprint(json.dumps(result, indent=2))\n```\n\n### Batch Processing Multiple Images\n\n```python\nimport pytesseract\nfrom PIL import Image\nimport json\nimport os\nfrom pathlib import Path\n\ndef process_image_directory(directory_path, output_file):\n    \"\"\"Process all images in a directory and save results.\"\"\"\n    image_extensions = {'.jpg', '.jpeg', '.png', '.webp'}\n    results = []\n\n    for file_path in sorted(Path(directory_path).iterdir()):\n        if file_path.suffix.lower() in image_extensions:\n            result = ocr_to_json(str(file_path))\n            results.append(result)\n            print(f\"Processed: {file_path.name}\")\n\n    # Save results\n    with open(output_file, 'w') as f:\n        json.dump(results, f, indent=2)\n\n    return results\n```\n\n\n## Tesseract Configuration Options\n\n### Language Selection\n\n```python\n# Specify language (default is English)\ntext = pytesseract.image_to_string(img, lang='eng')\n\n# Multiple languages\ntext = pytesseract.image_to_string(img, lang='eng+fra+deu')\n```\n\n### Page Segmentation Modes (PSM)\n\nUse `--psm` to control how Tesseract segments the image:\n\n```python\n# PSM 3: Fully automatic page segmentation (default)\ntext = pytesseract.image_to_string(img, config='--psm 3')\n\n# PSM 4: Assume single column of text\ntext = pytesseract.image_to_string(img, config='--psm 4')\n\n# PSM 6: Assume uniform block of text\ntext = pytesseract.image_to_string(img, config='--psm 6')\n\n# PSM 11: Sparse text - find as much text as possible\ntext = pytesseract.image_to_string(img, config='--psm 11')\n```\n\nCommon PSM values:\n- `0`: Orientation and script detection (OSD) only\n- `3`: Fully automatic page segmentation (default)\n- `4`: Single column of text of variable sizes\n- `6`: Uniform block of text\n- `7`: Single text line\n- `11`: Sparse text\n- `13`: Raw line\n\n\n## Image Preprocessing\n\nFor better OCR accuracy, preprocess images:\n\n```python\nfrom PIL import Image, ImageFilter, ImageOps\n\ndef preprocess_image(image_path):\n    \"\"\"Preprocess image for better OCR results.\"\"\"\n    img = Image.open(image_path)\n\n    # Convert to grayscale\n    img = img.convert('L')\n\n    # Increase contrast\n    img = ImageOps.autocontrast(img)\n\n    # Apply slight sharpening\n    img = img.filter(ImageFilter.SHARPEN)\n\n    return img\n\n# Use preprocessed image for OCR\nimg = preprocess_image(\"document.jpg\")\ntext = pytesseract.image_to_string(img)\n```\n\n### Advanced Preprocessing Strategies\n\nFor difficult images (low contrast, faded text, dark backgrounds), try multiple preprocessing approaches:\n\n1. **Grayscale + Autocontrast** - Basic enhancement for most images\n2. **Inverted** - Use `ImageOps.invert()` for dark backgrounds with light text\n3. **Scaling** - Upscale small images (e.g., 2x or 3x) before OCR to improve character recognition. For faint or thin fonts on receipts, 3x with autocontrast frequently recovers decimal points and punctuation that 1x\u002F2x lose.\n4. **Thresholding** - Convert to binary using `img.point(lambda p: 255 if p > threshold else 0)` with different threshold values (e.g., 100, 128)\n5. **Sharpening** - Apply `ImageFilter.SHARPEN` to improve edge clarity\n\n\n## Multi-Pass OCR Strategy\n\nFor challenging images, a single OCR pass may miss text. Use multiple passes with different configurations:\n\n1. **Try multiple PSM modes** - Different page segmentation modes work better for different layouts (e.g., `--psm 6` for blocks, `--psm 4` for columns, `--psm 11` for sparse text)\n\n2. **Try multiple preprocessing variants** - Run OCR on several preprocessed versions of the same image\n\n3. **Retain results** - Keep each pass's text and source separately for structured-field decisions. Concatenation can improve raw-text coverage, but it loses the evidence needed to select one field value.\n\n```python\ndef multi_pass_ocr(image_path):\n    \"\"\"Run OCR with multiple strategies and combine results.\"\"\"\n    img = Image.open(image_path)\n    gray = ImageOps.grayscale(img)\n    w, h = img.size\n\n    # Generate preprocessing variants (include an upscaled variant; 3x often\n    # recovers thin punctuation such as the decimal point in prices)\n    variants = [\n        ImageOps.autocontrast(gray),\n        ImageOps.invert(ImageOps.autocontrast(gray)),\n        gray.filter(ImageFilter.SHARPEN),\n        ImageOps.autocontrast(img.resize((w * 3, h * 3), Image.LANCZOS).convert('L')),\n    ]\n\n    # PSM modes to try\n    psm_modes = ['--psm 6', '--psm 4', '--psm 11']\n\n    all_text = []\n    for variant in variants:\n        for psm in psm_modes:\n            try:\n                text = pytesseract.image_to_string(variant, config=psm)\n                if text.strip():\n                    all_text.append(text)\n            except Exception:\n                pass\n\n    # Combine all extracted text\n    return \"\\n\".join(all_text)\n```\n\nThis approach improves extraction for receipts, faded documents, and images with varying quality.\n\n### Structured Field Decision Branches\n\nAfter one batch extraction, save provisional results and a separate evidence\nrecord containing each candidate's raw text, field label, image region, and OCR\nsource. Evaluate the following triggers for every field, including non-empty and\nformat-valid fields. Select a branch before selecting a value. A clear field can\nbe confirmed directly; OCR frequency, keyword rank, and variant order alone are\nnot confirmation evidence.\n\n#### Branch A — Uncertain printed date digit\n\n**Trigger:** the date's day, month, or year has faint\u002Fbroken strokes, loop-like\nmarks, two arcs with unclear closure, a digit touching a separator, or conflicting\nreadings. This branch also applies when all OCR variants agree but the printed\ncharacter remains uncertain. A calendar-valid result does not clear this trigger.\n\n**Execute:**\n1. Crop the complete date line with its label, separators, and neighboring date\n   components, retaining margins around the uncertain character. Use the original\n   image as well as an enlarged grayscale copy; thresholding can erase a gap or\n   join two strokes.\n2. View the saved crop with the available image-view tool (for example, the file\n   editor's image view). For a possible open-arc\u002Fclosed-loop confusion such as\n   `3` versus `8`, examine the left-side gaps and closures in the original pixels.\n   Compare nearby printed digits in the same font; ignore handwriting and OCR\n   frequency as substitutes for that comparison. Use line OCR on the crop for\n   additional readings, keeping them as alternatives rather than votes.\n3. Resolve the printed character and the day\u002Fmonth\u002Fyear order from the same date\n   field and receipt context. Record the crop and visual reason, then convert to\n   ISO. If the pixels remain inconclusive, keep the field pending instead of\n   replacing one legal month with another by a global substitution rule.\n\n**Confirm when:** the selected date components have identifiable source evidence,\nnot merely a valid format or more OCR occurrences. When no visual tool is\navailable, use focused OCR with retained context and acknowledge unresolved\nambiguity rather than claiming visual verification.\n\n#### Branch B — Inclusive total, rounding, and a later final total\n\n**Trigger:** the payment block contains a pre-round total (possibly labeled as\ntax-inclusive), a signed rounding adjustment, and a later `TOTAL`, `NET TOTAL`,\n`NETT TOTAL`, or other final-payable line. Different amounts in these roles are\nnot conflicting OCR readings of one field.\n\n**Execute:**\n1. Crop and read the whole payment block together. Assign candidates to separate\n   roles: subtotal, tax\u002Fservice components, pre-round payable, signed adjustment,\n   final payable, tendered cash, and change. Keep a tax-summary table outside this\n   payment block; its own `TOTAL` is not the amount due.\n2. Preserve the `+` or `-` on the adjustment. Parse these monetary tokens with\n   `Decimal` and check `final_payable == pre_round_payable + signed_adjustment`.\n   The sign is part of the value, not punctuation to discard. If the pre-round\n   payable already includes tax\u002Fservice charges, use those components only to\n   cross-check it, not to add them again.\n3. Select the explicitly printed final-payable amount when the field association\n   and arithmetic agree, even if an earlier tax-inclusive amount occurs in more\n   OCR passes or has a higher-ranked keyword. The final label can be larger or\n   bolder than surrounding text; inspect that line separately if OCR misses it.\n4. If a digit or separator is missing or the arithmetic disagrees, review the\n   corresponding local crop. Accept the result only after resolving the reading;\n   do not invent a rounding adjustment or silently force the arithmetic to fit.\n\n**Confirm when:** the final amount is anchored to its own label and verified\nagainst the printed settlement relation. If no rounding\u002Ffinal-total pattern is\npresent, resolve the final-payable role from the receipt's actual labels; do not\napply this branch's adjustment speculatively. Keywords such as `GST` identify a\npossible component, not an automatic reason to discard an inclusive payable.\n\n#### Branch C — Amount split across lines or repaired digits\n\n**Trigger:** an amount keyword has no number on its line, or a candidate relies\non inserting a decimal, removing an OCR character, or joining separated digits.\n\n**Execute:** crop the label and neighboring lines together. Locate the numeric\nregion that is visually associated with that label; exclude quantity, date,\nidentifier, tax-rate, and change fields. Use next-line\u002Ftrailing tokens only as\ncandidates. Support each punctuation\u002Fdigit repair from the original monetary\nregion, and then apply Branch B if settlement roles are present.\n\n**Confirm when:** both the label association and repaired number are supported.\nNeither the last number on a line nor successful parsing clears this branch.\n\n#### Branch D — Reusing confirmed fields after a parser change\n\n**Trigger:** changing shared parsing\u002Fselection logic, adding a raw-OCR preference,\nor retrying extraction after any fields have been confirmed.\n\n**Execute:** save and reload the confirmed field values with their evidence before\nthe change. Run the new logic into provisional candidates. Merge only reviewed\nupdates to unresolved or demonstrably affected fields; retain every other\nconfirmed field. A changed candidate requires stronger same-field evidence and\na recorded reason before replacing a confirmed value. Rebuild the deliverable\nfrom this merged accepted state, not from fresh unreviewed batch output.\n\n**Confirm when:** a before\u002Fafter field comparison shows that every replacement\nhas been reviewed. Execute the write, reopen the destination workbook, and compare\nits stored values with the accepted state. Logs or values left only in memory do\nnot demonstrate delivery. Reserve budget for this final readback instead of\nunnecessarily repeating full-batch OCR at the end.\n\n### Numeric Normalization for Receipts and Forms\n\nOCR frequently corrupts decimal separators and digit spacing in monetary amounts.\nApply the following repairs only inside a monetary region associated with its\nlabel, and only when its printed evidence supports the repair (Branch C):\n\n- Replace `:` between digits with `.` (e.g., `92:80` -> `92.80`).\n- Collapse a single space between a 1-4 digit group and a 2-digit group into `.` (e.g., `92 80` -> `92.80`, `1 234 56` -> keep the comma-thousands intact but treat trailing ` 56` as decimals only when no other decimal separator is present).\n- Reinterpret bare digit clusters of length >=3 whose last two digits look like cents as `X.YY` when no decimal separator was recognized (e.g., `9280` -> `92.80`) **only** when the line contains a strong keyword such as `GRAND TOTAL` and no better candidate exists.\n- Remove clearly extraneous surrounding OCR marks; retain decimal\u002Fthousands punctuation and any genuine sign, especially on rounding adjustments.\n- Accept commas as thousands separators: `1,234.56` -> `1234.56`.\n\nPrefer a well-formed monetary token in the identified field. Next-line candidates\nand decimal repairs must pass Branch C; this helper parses a token but does not\ndecide whether it is the final amount. Keep date and ID parsing separate.\n\n```python\nimport re\nfrom decimal import Decimal\n\nMONEY_STRICT = re.compile(r'[+-]?(?:\\d{1,3}(?:,\\d{3})+|\\d+)\\.\\d{2}')\n\ndef parse_money(token: str, *, decimal_repair_supported=False):\n    \"\"\"Parse a field-associated monetary token, preserving a printed sign.\"\"\"\n    if token is None:\n        return None\n    s = token.strip().replace('\\u2212', '-')\n    if MONEY_STRICT.fullmatch(s):\n        return Decimal(s.replace(',', ''))\n    if not decimal_repair_supported:\n        return None\n    # Enable only after the original region establishes the decimal position.\n    candidates = [\n        re.sub(r'(\\d):(\\d{2})$', r'\\1.\\2', s),\n        re.sub(r'(\\d)\\s+(\\d{2})$', r'\\1.\\2', s),\n    ]\n    if re.fullmatch(r'[+-]?\\d{3,}', s):\n        candidates.append(s[:-2] + '.' + s[-2:])\n    for candidate in candidates:\n        if MONEY_STRICT.fullmatch(candidate):\n            return Decimal(candidate.replace(',', ''))\n    return None\n```\n\n\n## Error Handling\n\n### Common Issues and Solutions\n\n**Issue**: Tesseract not found\n\n```python\n# Verify Tesseract is installed\ntry:\n    pytesseract.get_tesseract_version()\nexcept pytesseract.TesseractNotFoundError:\n    print(\"Tesseract is not installed or not in PATH\")\n```\n\n**Issue**: Poor OCR quality\n\n- Preprocess image (grayscale, contrast, sharpen)\n- Use appropriate PSM mode for the document type\n- Ensure image resolution is sufficient (300+ DPI)\n\n**Issue**: Empty or garbage output\n\n- Check if image contains actual text\n- Try different PSM modes\n- Verify image is not corrupted\n\n**Issue**: Keyword detected but no numeric value parsed\n\n- Do not immediately return null. First:\n  1. Run Branch C on the label and its neighboring numeric region.\n  2. If multiple settlement amounts or rounding appear, run Branch B.\n  3. Retain unresolved candidates and continue focused review within the available budget.\n- If the field remains unresolved, report extraction failure instead of accepting\n  an unrelated but parseable number. Non-empty ambiguous fields use the same\n  decision branches; review is not limited to null values.\n\n\n## Quality Self-Check\n\nBefore returning results, verify:\n\n- [ ] Output is valid JSON (use `json.loads()` to validate)\n- [ ] All required fields are present (`success`, `filename`, `extracted_text`, `confidence`, `metadata`)\n- [ ] Text preserves logical reading order\n- [ ] Confidence level reflects actual OCR quality\n- [ ] Warnings array includes all detected issues\n- [ ] Special characters are properly escaped in JSON\n\n### Structured-Value Extraction Self-Check\n\nWhen the caller extracts specific fields (amounts, dates, IDs), also verify:\n\n- [ ] For every image where a target keyword (e.g., `GRAND TOTAL`, `TOTAL RM`) is detected in **any** OCR variant, a candidate value was produced, or the reason for failure is explicitly logged.\n- [ ] Uncertain printed date components passed Branch A, including legal dates with weak source readings and correlated OCR agreement.\n- [ ] Settlement totals passed Branch B where applicable; pre-round, rounded, tax-summary, tendered, and change amounts were kept in separate roles.\n- [ ] Decimal repairs and split-line candidates passed Branch C, preserving a genuine adjustment sign.\n- [ ] Parser changes and retries passed Branch D; confirmed fields were preserved unless an evidenced correction was accepted.\n- [ ] The executed script produced the requested file, and readback matched the accepted fields and caller's schema, coverage, and ordering.\n\n\n## Limitations\n\n- Tesseract works best with printed text; handwriting recognition is limited\n- Accuracy decreases with decorative fonts, artistic text, or extreme stylization\n- Mathematical equations and special notation may not extract accurately\n- Redacted or watermarked text cannot be recovered\n- Severe image degradation (blur, noise, low resolution) reduces accuracy\n- Complex multi-column layouts may require custom PSM configuration\n- Thin punctuation (decimal points, colons) is often lost or substituted; always attempt numeric normalization before rejecting a value\n\n\n## Version History\n\n- **1.0.0** (2026-01-13): Initial release with Tesseract\u002Fpytesseract OCR\n- **1.1.0**: Added value-level voting across variants, numeric normalization guidance for OCR-corrupted amounts, and structured-value extraction self-check.\n",{"name":5,"description":7},"main","cdcb6d230f3b15c3c33f8a2bdd16439f43602cbe","evaluation\u002Fresults\u002F对于jpg-ocr-stat的尝试\u002Fruns\u002Fjpg-ocr-stat-skillaxe-branches-round23-opus80-20261012-r001-recovery001\u002Finputs\u002Fskills\u002Fimage-ocr",1,"https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark","https:\u002F\u002Fgithub.com\u002Flinyouxue","2026-11-14 20:28:35",{"source":8,"skillName":5,"commands":32,"prompt":42,"zipAvailable":43},[33,36,39],{"tool":34,"command":35},"npx","npx skills add linyouxue\u002Fskill-repair-benchmark --skill image-ocr",{"tool":37,"command":38},"bunx","bunx skills add linyouxue\u002Fskill-repair-benchmark --skill image-ocr",{"tool":40,"command":41},"pnpm","pnpm dlx skills add linyouxue\u002Fskill-repair-benchmark --skill image-ocr","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fevaluation\u002Fresults\u002F对于jpg-ocr-stat的尝试\u002Fruns\u002Fjpg-ocr-stat-skillaxe-branches-round23-opus80-20261012-r001-recovery001\u002Finputs\u002Fskills\u002Fimage-ocr\u002FSKILL.md",true,[],[46,53,60,66,71,76],{"skillNo":47,"name":48,"nameZh":6,"description":49,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":50,"categories":51,"keywords":52,"publishedAt":19},"01M7E1HGVRY73NBJ022CJG13X6","video-frame-extraction","Extract frames from video files and save them as images using OpenCV","https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fjpg-ocr-stat\u002Fresult\u002Fskilltta-claude4.7\u002Fgroup_03\u002Frun_03\u002Finputs\u002Fskills\u002Fvideo-frame-extraction\u002FSKILL.md",[],[],{"skillNo":54,"name":55,"nameZh":6,"description":56,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":57,"categories":58,"keywords":59,"publishedAt":19},"01M7E1HAG5R7CJ9C8NYJS771MB","openai-vision","Analyze images and multi-frame sequences using OpenAI GPT vision models","https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fjpg-ocr-stat\u002Fresult\u002Fskilltta-claude4.7\u002Fgroup_03\u002Frun_03\u002Finputs\u002Fskills\u002Fopenai-vision\u002FSKILL.md",[],[],{"skillNo":61,"name":5,"nameZh":6,"description":62,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":63,"categories":64,"keywords":65,"publishedAt":19},"01M7E1H7DADKB6QBWKC2KQ63SZ","Extract text content from images using Tesseract OCR via Python, including structured field extraction from receipts and forms","https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fjpg-ocr-stat\u002Fresult\u002Fskilltta-claude4.7\u002Fgroup_03\u002Frun_03\u002Finputs\u002Fskills\u002Fimage-ocr\u002FSKILL.md",[],[],{"skillNo":67,"name":48,"nameZh":6,"description":49,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":68,"categories":69,"keywords":70,"publishedAt":19},"01M7E1H0WBNT43TENGQY7AVZHD","https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fjpg-ocr-stat\u002Fresult\u002Fskilltta-claude4.7\u002Fgroup_03\u002Frun_02\u002Finputs\u002Fskills\u002Fvideo-frame-extraction\u002FSKILL.md",[],[],{"skillNo":72,"name":55,"nameZh":6,"description":56,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":73,"categories":74,"keywords":75,"publishedAt":19},"01M7E1GTC7D4N4Q2EF03875J9E","https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fjpg-ocr-stat\u002Fresult\u002Fskilltta-claude4.7\u002Fgroup_03\u002Frun_02\u002Finputs\u002Fskills\u002Fopenai-vision\u002FSKILL.md",[],[],{"skillNo":77,"name":5,"nameZh":6,"description":62,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":80,"publishedAt":19},"01M7E1GQ92CWFMGH53KSSE4WER","https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fjpg-ocr-stat\u002Fresult\u002Fskilltta-claude4.7\u002Fgroup_03\u002Frun_02\u002Finputs\u002Fskills\u002Fimage-ocr\u002FSKILL.md",[],[]]