hebrew-ocr-forms

A process for reading scanned Israeli government forms with optical character recognition, or OCR, which turns text in images into editable text. It identifies common Hebrew forms and extracts fields such as names, amounts, dates, and registration details.

In plain words
What is it for?
Process forms such as land-registry extracts, tax documents, National Insurance documents, and vehicle licenses, then extract their important fields.
Why use it?
Scanned forms cannot be searched or processed like normal text, and Hebrew scans may need image cleanup before their fields can be read reliably.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/squadcodercom/squadcoder/hebrew-ocr-forms
Any agent
npx skills add squadcodercom/squadcoder --skill hebrew-ocr-forms
Clone the repo
git clone --depth 1 https://github.com/squadcodercom/squadcoder

Made for: Claude Code, Codex.

Per session 183 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,117 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00183 $0.03117
Opus 5 $0.00092 $0.01558
Sonnet 5 $0.00037 $0.00623
Haiku 4.5 $0.00018 $0.00312

Measured yesterday against content hash 72ae1825e18e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade B, and why

hebrew-ocr-forms scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/extract_form_fields.py, scripts/preprocess_image.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Asks for rootmediumPrivilege escalation

A mod that escalates privileges can change anything on the machine, not only the project.

Solution: Install with `sudo apt-get install tesseract-ocr-heb` (Ubuntu) or `brew install tesseract-lang` (macOS). Verify with `tesseract --list-langs`.
.squadcoder/skills/hebrew-ocr-forms/SKILL.md · 186 lines

How it starts

The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

Read the full file on GitHub · 186 lines

Files

What ships with it

6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 186 lines · 183 tokens per session scan B 72ae1825e18e

Subscribe to this mod's changes

hebrew-ocr-forms is a skill published in the GitHub repository squadcodercom/squadcoder (11 stars, last pushed 2mo ago), licensed MIT. It adds 183 tokens to every session and 3,117 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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