NacerKr/colors-normalized

Color Names Normalized

A dataset for normalizing messy, multilingual, free-text color names to a small, fixed vocabulary. Real-world color attributes — product feeds, marketplace listings, survey answers — are free text with thousands of variants. This dataset maps 38,112 real-world color names (English and French) to a strict 20-color base palette — e.g. "rouge", "dark navy", "burgundy", "whispering grasslands" all resolve to a canonical base color — so color data becomes groupable, filterable, and analyzable.

Each color name comes with its hex code and was assigned to the perceptually closest palette color using the CIEDE2000 color-difference formula in CIELAB space (D65).

Use cases

• E-commerce catalogs: power color filters and facets from free-text product attributes instead of maintaining hand-written mapping tables.
• Analytics: aggregate sales or inventory by 20 base colors rather than thousands of vendor-specific shade names.
• Search & matching: treat "navy", "midnight", and "bleu marine" as the same bucket for retrieval, dedup, or entity matching.
• ML features: turn a high-cardinality text column into a clean 20-class categorical feature.

Quick start

import pandas as pd

# Load directly from the Hub
df = pd.read_parquet(
    "hf://datasets/NacerKr/colors-normalized/data/colors_normalized.parquet"
)

# Build a normalization lookup: messy name -> base color
lookup = df.set_index(["name", "language"])["base_color"]
lookup.loc[("rouge", "fr")]      # 'red'
lookup.loc[("burgundy", "en")]   # 'brown'

Or with the datasets library:

from datasets import load_dataset

ds = load_dataset("NacerKr/colors-normalized", split="train")

Names in the dataset are lowercased and whitespace-normalized — apply .str.strip().str.lower() (and collapse inner whitespace) to your raw color values before joining.

Schema

Column	Type	Description
name	string	Color name, lowercased and whitespace-normalized
hex	string	Hex code of the named color, uppercase #RRGGBB
language	string	en or fr
source	string	color-names, color-pedia, or french-gist
base_color	string	Assigned base palette color (one of 20 labels)
base_hex	string	Hex code of the assigned base color
delta_e	float32	CIEDE2000 distance between hex and base_hex (0 = exact)

38,112 rows — 37,877 English, 235 French. Median ΔE₀₀ to the assigned base color is 12.4 (p90 = 19.6). Unique on (name, language).

Base palette (20 labels)

The palette is the basic palette from colorjs/color-namer (MIT), minus fuchsia, which is an exact duplicate of magenta (#FF00FF).

Label	Hex		Label	Hex
black	#000000		tan	#D2B48C
blue	#0000FF		violet	#EE82EE
cyan	#00FFFF		beige	#F5F5DC
green	#008000		gold	#FFD700
teal	#008080		magenta	#FF00FF
turquoise	#40E0D0		orange	#FFA500
indigo	#4B0082		pink	#FFC0CB
gray	#808080		red	#FF0000
purple	#800080		white	#FFFFFF
brown	#A52A2A		yellow	#FFFF00

Methodology

1. Merge three permissively-licensed color-name sources (below) into (name, hex) pairs tagged with language and source.
2. Clean: lowercase and whitespace-normalize names; validate and normalize hex to uppercase #RRGGBB; drop junk names that embed raw hex codes (~15k generation artifacts in color-pedia); deduplicate on (name, language) with source priority color-names → color-pedia → french-gist.
3. Assign: convert every hex and the 20 palette anchors from sRGB to CIELAB (D65 white point), compute the full N×20 CIEDE2000 distance matrix in one vectorized numpy pass, and take the argmin. The CIEDE2000 implementation is validated against the Sharma et al. (2005) published test pairs.

Sources & attribution

Source	Contribution	License
boltuix/color-pedia	English color names + hex	MIT
BatteRaquette58/color-names (derived from meodai/color-names)	English color names + hex	MIT
angelodlfrtr's french_colors.json gist	French color names + hex	not stated (attribution given)
colorjs/color-namer	20-label base palette	MIT

The EPFL Multi-lingual Color Thesaurus was evaluated as an additional French source but deliberately excluded: its CC BY-NC-SA 3.0 license (non-commercial) is incompatible with commercial use.

Limitations

• Nearest-anchor artifacts: assignment is purely geometric against 20 fixed anchor points. A few results differ from human intuition — e.g. navy blue (#000080) lands on indigo rather than blue, and some saturated pinks land on violet/magenta because the pink anchor (#FFC0CB) is very pale. Use delta_e as a confidence signal (larger = less certain).
• French coverage is small (235 names) relative to English.
• Names are lowercased; match case-insensitively against your raw data.

License

Released under the MIT License.