fast-plate-ocr 1.1.0

Highlights

Added

• New cct-xs-v2-global-model and cct-s-v2-global-model with plate region recognition support for 65+ countries.
• Optional region-aware training, validation, and inference flow, including plate_region dataset support.
• New region evaluation metrics, including val_region_macro_f1, plus per-region evaluation in validation.
• New CCT v2 model configs, parameterized plate configs, and focal loss support for region classification.
• Inference now returns PlatePrediction, exposing region outputs when available.
• Export pipeline improvements for multi-output models, plus support for selecting the ONNX opset version.
• Expanded dataset validation and annotation checks, including region validation and warnings on unexpected columns.

Changed

• V2 pre-trained models were trained on roughly 3x more data than the v1 generation.
• V2 training now includes empty plates built from backgrounds, noise, textures, and other padded-plate negatives.
• Region recognition now includes an Unknown class trained mainly with synthetic data.
• Updated the new CCT v2 models to use silu instead of gelu to avoid export issues with some library versions.
• Added the corrected attention_layout behavior so split projection dimensions are distributed per head instead of reusing the full projection_dim.
• The shipped v2 xs and s pre-trained models both exceed 0.99 val_region_macro_f1 on a held-out validation split with more than 114_000 samples.
• Transformer blocks and training defaults are more configurable, including projection validation, loss weighting, and milder augmentations.
• Inference now removes the pad character by default from decoded output.
• TFLite export now uses LiteRT via ai-edge-litert following TensorFlow deprecation changes.

Fixed

• Fixed region recognition mismatches during validation.
• Fixed EarlyStopping metric selection when training single-head models.
• Fixed learning-rate decay step calculation to account for warmup steps.

Full Changelog: v1.0.2...v1.1.0