tejas-label-ocr

Lightweight, stateless Python library for extracting structured data from product packaging labels using OCR and LLM-based parsing.

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Features

Stateless & In-Memory: Processes raw image bytes—no disk I/O, ideal for serverless (FastAPI/Flask).
OCR + LLM Powered: Uses PaddleOCR for text extraction and Mistral AI (mistral-small-latest) for structured parsing.
Noise Filtering: Automatically cleans low-confidence OCR text and groups spatial blocks (rows/columns/proximity).
Flexible Auth: Pass api_key directly or via MISTRAL_API_KEY environment variable.
Structured JSON Output: Extracts brand, product name, ingredients, nutrition facts, allergens, and more.

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Installation

pip install tejas-label-ocr

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Usage

Front Label Processing

from label_ocr import process_front_label

with open("product_label.jpg", "rb") as f:
    image_bytes = f.read()

result = process_front_label(image_bytes, api_key="your-mistral-api-key")
print(result)
# Output: {"brand": "Nestlé", "product_name": "Organic Oats", "claims": ["Gluten-Free", "Non-GMO"], ...}

Nutrition Label Processing

from label_ocr import process_nutrition_label

with open("nutrition_panel.jpg", "rb") as f:
    image_bytes = f.read()

result = process_nutrition_label(image_bytes, api_key="your-mistral-api-key")
print(result)
# Output: {"serving_size": "30g", "nutrients": [{"name": "Calories", "value": 120, "unit": "kcal", "%RDA": 6}], ...}

— Notes —–

Requires a Mistral AI API key (sign up at Mistral AI).
For best results, use high-resolution images of labels with clear text.

Metadata

Release files for tejas-label-ocr 0.1.7

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Source distribution (sdist)

Source distribution for tejas-label-ocr 0.1.7
File	Size	Uploaded
tejas_label_ocr-0.1.7.tar.gz	8.0 kB	Aug 5, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for tejas-label-ocr 0.1.7
File	Interpreter	ABI	Platform	Reset
tejas_label_ocr-0.1.7-py3-none-any.whl 8.3 kB Aug 5, 2026	Python 3	none	any	Details

Total release size: 16.4 kB

Release files / tejas_label_ocr-0.1.7.tar.gz

Download URL	tejas_label_ocr-0.1.7.tar.gz
Size	8.0 kB
Tags	Source
SHA-256 checksum How to use checksums	`43a0e05e36b35f57908c193c01e3bc9f69e313d0aea550bec0104b8343933cb8`
BLAKE2b-256 checksum How to use checksums	`5e480ba7848b227a045988900c3622398f096c55a61fad90a453619d09fb6e08`
Upload date	Aug 5, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.12.9`

Release files / tejas_label_ocr-0.1.7-py3-none-any.whl

Download URL	tejas_label_ocr-0.1.7-py3-none-any.whl
Size	8.3 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`d2e992b32286b7a4b7eb7c53c5c5c15cc9488a94097b9044e1adc342687cdb53`
BLAKE2b-256 checksum How to use checksums	`45a2563edf7f3cda4245ad1a2aa16d092aeda6d4ee132a2d836336c3722373ca`
Upload date	Aug 5, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.12.9`