hebrew-ocr-forms

name	hebrew-ocr-forms
description	Process and extract data from scanned Israeli government forms using OCR. Supports Tabu (land registry), Tax Authority forms, Bituach Leumi documents, and other official Israeli paperwork. Use when user asks to OCR Hebrew documents, extract data from Israeli forms, "lesarek tofes", parse Tabu extract, read scanned tax form, or process Israeli government documents. Includes Hebrew OCR configuration, field extraction patterns, and RTL text handling. Activate for: סריקת מסמך, לסרוק טופס, חילוץ נתונים מטופס, OCR בעברית, טאבו, נסח טאבו, טופס רשות המסים, מסמכי ביטוח לאומי, קריאת טופס סרוק, טפסים ממשלתיים. Do NOT use for handwritten Hebrew recognition (requires specialized models) or non-Israeli form processing.
license	MIT
allowed-tools	Bash(python:*) Bash(pip:*) Bash(tesseract:*)
compatibility	Requires Tesseract OCR with Hebrew language pack. Python with pytesseract, Pillow, and opencv-python.

Hebrew OCR Forms

Instructions

Step 1: Identify the Form Type

Form Type	Source	Key Identifiers	Common Fields
Nesach Tabu	Land Registry	"נסח טאבו", "לשכת רישום המקרקעין"	Gush, Chelka, Owner, Liens
Tofes 106	Tax Authority	"טופס 106", "דו״ח שנתי למעביד"	Salary, Tax, Employer
Ishur Nikui	Tax Authority	"אישור ניכוי מס במקור"	Tax rate, Validity, TZ
Tofes 857	Tax Authority	"טופס 857", "רווח הון"	Transaction, Gain, Tax
Ishur Zkauyot	Bituach Leumi	"אישור זכאויות", "ביטוח לאומי"	Benefit type, Amount
Tofes 100	Bituach Leumi	"טופס 100", "דין וחשבון"	Employees, Wages
Rishayon Rechev	Vehicle Licensing	"רישיון רכב"	Plate, Owner, Expiry

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)

Strip nikud before exact-string matching. normalize_bidi_text() only removes Unicode category Cf (format) characters. Hebrew nikud (vowel diacritics, U+0591 to U+05C7) are category Mn (non-spacing mark), so they survive this filter. If a scanned form happens to contain vocalized text, or the OCR engine hallucinates stray marks, an exact-string or regex keyword match like "גוש" will silently fail against "גּוּשׁ". Strip nikud explicitly before keyword matching:

import re

NIKUD_RE = re.compile(r'[֑-ׇ]')

def strip_nikud(text):
    """Remove Hebrew diacritics so keyword regexes match reliably."""
    return NIKUD_RE.sub('', text)

Normalize final-form letters when matching. Hebrew has five final (sofit) letter forms: ך ם ן ף ץ. They are the same letters as כ מ נ פ צ but are encoded as distinct code points. OCR usually preserves them correctly, but keyword and fuzzy-match logic can break: a label OCR'd with a final form will not equal a search term written with the medial form, and vice versa. When matching field labels, normalize both sides to a single form first:

SOFIT_MAP = {'ך': 'כ', 'ם': 'מ', 'ן': 'נ', 'ף': 'פ', 'ץ': 'צ'}

def normalize_sofit(text):
    """Fold final-form letters to their medial forms for matching."""
    return ''.join(SOFIT_MAP.get(ch, ch) for ch in text)

Apply strip_nikud() and normalize_sofit() only to the matching key, never to the value you store or display, the final forms and any nikud are part of correct Hebrew and must be preserved in output.

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

Examples

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.

Bundled Resources

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.

OCR Engine Options

Tesseract is a strong free baseline for Hebrew print, but modern cloud vision APIs often match or beat it on noisy scans and stamped forms. Pick per use case:

Engine	Hebrew print	Handwriting	Cost model	When to use
Tesseract (heb+eng, LSTM)	Good for clean scans at 300 DPI+	Weak	Free, self-hosted	Default for batch local pipelines, privacy-sensitive data
Google Cloud Vision – Document Text Detection	Very good, robust to noise	Partial (printed-looking handwriting only)	Per-request	Mixed-quality scans, large batches, PDF forms
AWS Textract (AnalyzeDocument)	NOT supported (Latin-script languages only: EN/ES/DE/IT/FR/PT)	Not for Hebrew	Per-page	Do NOT use for Hebrew forms; its text/forms extraction returns garbage on Hebrew
Azure AI Vision – Read API	Not in the officially-listed printed-OCR set; verify on your own documents (community reports of gibberish on Hebrew)	Not reliable for Hebrew	Per-transaction	Test before relying on it for Hebrew; strong for Latin/enterprise PDFs
Claude Vision (claude-sonnet-4-6 / claude-opus-4-8)	Very good, context-aware	Good (with prompt guidance)	Token-based	Unusual form layouts, cross-field validation, when you want the model to also reason about the data

Notes: none of these engines is reliable for cursive handwritten Hebrew on forms like old Tabu extracts. For those, flag for human review instead of auto-extraction. When you do call Google Cloud Vision for Hebrew, pass languageHints: ['iw'] (Vision uses the legacy ISO code iw for Hebrew, not he); Tesseract uses heb. AWS Textract and Azure Read are not dependable for Hebrew, prefer Tesseract (heb), Google Cloud Vision (iw), or Claude Vision.

Gotchas

• Hebrew OCR accuracy drops significantly for handwritten text, especially for the letters vav, zayin, and yod which look similar. Always include confidence scores and flag low-confidence characters.
• Scanned Israeli government forms often have mixed Hebrew and Arabic text. Agents may OCR the Arabic sections as Hebrew or skip them entirely. Both languages use RTL but different Unicode ranges.
• Israeli ID numbers (mispar zehut) and phone numbers extracted via OCR should be validated with check-digit algorithms after extraction, as single-digit OCR errors are common.
• Many Israeli forms use dot-matrix printed Hebrew, which has lower OCR accuracy than laser-printed text. Agents may report higher confidence than warranted for older government documents.
• Tesseract can reorder digit runs inside an RTL line. A number like a 9-digit TZ or an invoice amount printed inside a Hebrew sentence may come back with its digit groups shuffled, even when each individual digit was read correctly. Do not trust the raw left-to-right order of numbers extracted from a mixed Hebrew/digit line, re-anchor each number to its field label and re-validate with check-digit or range rules.
• The bundled scripts only accept a single image file. Many Israeli forms are multi-page PDFs (a Tofes 106 packet, a multi-sheet Tabu extract). Convert each PDF page to an image first, for example with pdf2image (convert_from_path) or pdftoppm, then run preprocessing and OCR per page and merge the extracted fields. Do not feed a raw PDF to preprocess_image.py or extract_form_fields.py.

Reference Links

Source	URL	What to Check
Tesseract documentation	https://tesseract-ocr.github.io/tessdoc/	Installation, language packs, LSTM mode, PSM values
Tesseract Hebrew traineddata	https://github.com/tesseract-ocr/tessdata	Hebrew model for tesseract
Google Cloud Vision – Documents	https://cloud.google.com/vision/docs/fulltext-annotations	Document Text Detection API reference
AWS Textract – AnalyzeDocument	https://docs.aws.amazon.com/textract/latest/dg/how-it-works-analyzing.html	Forms and tables extraction
Azure AI Vision – Read API	https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/language-support	Multi-language OCR; check the language-support list (Hebrew is not in the official printed-OCR set, verify before relying on it)
Israeli ID check-digit spec	https://he.wikipedia.org/wiki/מספר_זהות_(ישראל)	Algorithm for validating a 9-digit Israeli ID

Troubleshooting

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.