Hebrew OCR Forms

Step 1: Identify the Form Type

Step 2: Preprocess the Scanned Image

See scripts/preprocess_image.py for the full preprocessing pipeline. Key steps:

1. Convert to grayscale
2. Deskew -- Israeli forms are often slightly rotated from scanning
3. Binarize with adaptive threshold -- handles uneven lighting from scanners
4. Remove noise with morphological operations

Step 3: Run Hebrew OCR with Tesseract

See scripts/extract_form_fields.py for the full extraction pipeline.

Tesseract configuration for Hebrew forms:

config = (
    '--oem 1 '          # LSTM neural net (best for Hebrew)
    '--psm 6 '          # Assume uniform block of text
    '-l heb+eng '       # Hebrew + English (forms have both)
    '-c preserve_interword_spaces=1'  # Keep spacing for field alignment
)

• For tabular forms (Tabu, Tofes 106), use PSM 4 instead of PSM 6
• Always use LSTM mode (--oem 1) for best Hebrew accuracy
• Include both heb and eng languages since forms mix Hebrew and English/numbers

Step 4: Extract Fields by Form Type

Tabu Extract (Nesach Tabu) key fields:

• Gush (block) number: look for "גוש" followed by digits
• Chelka (parcel) number: look for "חלקה" followed by digits
• Owner name: follows "בעלים" or "שם הבעלים"
• ID number (TZ): follows "ת.ז." or "מספר זהות", 9 digits

Tax Form (Tofes 106) key fields:

• Tax year: follows "שנת מס"
• Employer number: follows "מספר מעביד" or "מס׳ עוסק", 9 digits
• Gross salary: follows "שכר ברוטו" or "הכנסה חייבת"
• Tax deducted: follows "מס שנוכה" or "ניכוי מס"

Step 5: Handle RTL and Bidirectional Text

import unicodedata

def normalize_bidi_text(text):
    """Normalize bidirectional text from Hebrew OCR output."""
    lines = text.split('\n')
    normalized = []
    for line in lines:
        # Strip bidi control characters
        clean = ''.join(
            c for c in line
            if unicodedata.category(c) != 'Cf'
        )
        clean = ' '.join(clean.split())
        if clean:
            normalized.append(clean)
    return '\n'.join(normalized)

Step 6: Validate Extracted Data

After extraction, validate key fields:

• TZ numbers: Run through Israeli ID validation algorithm (check digit)
• Dates: Verify DD/MM/YYYY format and valid date ranges
• Amounts: Check that numeric fields parse correctly, no OCR artifacts in digits
• Gush/Chelka: Verify numeric format, reasonable ranges
• Cross-reference: If TZ appears in multiple fields, verify consistency

Example 1: Process Tabu Extract

User says: "Extract data from this scanned Tabu document" Result: Preprocess image, run Hebrew OCR, identify as Nesach Tabu, extract gush/chelka/owner/rights fields, validate TZ, return structured JSON.

Example 2: Batch Process Tax Forms

User says: "I have 50 scanned Tofes 106 forms -- extract salary and tax data" Result: Set up batch pipeline -- preprocess each image, OCR with Hebrew+English, extract Tofes 106 fields, validate, output to CSV/JSON with confidence scores.

Example 3: OCR Quality Issues

User says: "The OCR isn't reading the Hebrew text correctly" Result: Diagnose preprocessing -- check image resolution (recommend 300 DPI minimum), verify deskewing, adjust binarization threshold, try different PSM modes, check Hebrew language pack installation.

Scripts

• scripts/preprocess_image.py — Prepare scanned Israeli form images for OCR: grayscale conversion, deskewing rotated scans, adaptive binarization for uneven lighting, morphological noise removal, optional CLAHE contrast enhancement, and border removal. Run: python scripts/preprocess_image.py --help
• scripts/extract_form_fields.py — Run Tesseract Hebrew OCR on preprocessed form images and extract structured fields by form type. Supports auto-detection of Tabu, Tofes 106, and other Israeli government forms. Outputs JSON with extracted fields and Israeli ID validation. Run: python scripts/extract_form_fields.py --help

References

• references/israeli-form-types.md — Detailed catalog of Israeli government form types (Tabu/land registry, Tax Authority forms, Bituach Leumi documents) with field descriptions, regex extraction patterns, ID validation rules, date/currency formats, and OCR tips per form layout. Consult when identifying an unknown form or building field extraction logic for a specific document type.

Error: "Tesseract Hebrew language pack not found"

Cause: Hebrew traineddata not installed Solution: Install with sudo apt-get install tesseract-ocr-heb (Ubuntu) or brew install tesseract-lang (macOS). Verify with tesseract --list-langs.

Error: "OCR output is garbled or reversed"

Cause: Bidirectional text ordering issue Solution: Use normalize_bidi_text() function. Ensure Tesseract is using --oem 1 (LSTM mode). Check that the image is not mirrored.

Error: "Low accuracy on specific form sections"

Cause: Poor scan quality, stamps/signatures overlapping text, colored backgrounds Solution: Increase preprocessing -- apply stronger denoising, crop to specific form regions, use ROI-based extraction for known form layouts.