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 0.1.6

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Source distribution (sdist)

Source distribution for tejas-label-ocr 0.1.6
File	Size	Uploaded
tejas_label_ocr-0.1.6.tar.gz	8.0 kB	Aug 5, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for tejas-label-ocr 0.1.6
File	Interpreter	ABI	Platform	Reset
tejas_label_ocr-0.1.6-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-0.1.6.tar.gz

Download URL	tejas_label_ocr-0.1.6.tar.gz
Size	8.0 kB
Tags	Source
SHA-256 checksum How to use checksums	`b3326bed7216fcef19009df194d5d2a4fd6d22c5bc2b9c98eff8c222ea466546`
BLAKE2b-256 checksum How to use checksums	`bf06b08b540a577ba68278056e45b8574530cd08508834b0c8bda2620e5fb94e`
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-0.1.6-py3-none-any.whl

Download URL	tejas_label_ocr-0.1.6-py3-none-any.whl
Size	8.3 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`aca2e8ed75757fe8bf721d3374d760878a326a5045adf9a6a5e2d2b4c3b1deb0`
BLAKE2b-256 checksum How to use checksums	`55782cf31673be6043d3aebfe3a0b0231a8fdc79c1f42efab64de9f84b4da80b`
Upload date	Aug 5, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.12.9`