ArabicWordsEmbedding

Fine-tuned from google/embeddinggemma-2 (440M parameters with the audio encoder removed).

Training

Real data: about 400k word crops from 3,000 scanned pages of classical Arabic printed books (seemorg/books-ocr, human-reviewed transcriptions). Word boxes come from Tesseract and are aligned to the transcription.
Synthetic data: generated on the fly in about 50 fonts. Each word is rendered with full, partial or no tashkeel, attached punctuation and neighbouring words, then cropped like a word detector would and degraded like a scan (low resolution, blur, JPEG, binarisation, noise, rotation).
Objective: contrastive (InfoNCE). Every batch contains lookalike words (same letters without dots), plus targeted pairs where tashkeel or punctuation imitates a dot.
Method: LoRA on the vision tower and the transformer, merged into this checkpoint.

Trained and tested on printed Arabic book pages. Handwriting, scene text and decorative fonts were not evaluated.
Real-data boxes came from Tesseract. A detector that cuts boxes very differently (much tighter, or several words per box) may need a quick check.
Short words that differ by one prefix letter (ل / ال / و) cause most of the remaining real errors.
Matching inside one page is the intended use. Open-vocabulary recognition is weaker: 93.6% top-1 among 31.7k words.

Apache 2.0, the same as the base model google/embeddinggemma-2. Training pages come from seemorg/books-ocr (MIT).