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README.md

@@ -35,6 +35,7 @@ Hexagon V79 HTP NPU**.
 | `pp_ocrv5_lat_rec.tflite` | Latin text recognition (SVTR/CTC) | 2.2 MB | **0.31 ms** |
 | `zh_dict.txt` | 18,383-char ZH dictionary (PaddleOCR `ppocrv5_dict.txt`) | — | — |
 | `ko_dict.txt` | 11,945-char KO dictionary (PaddleOCR `ppocrv5_korean_dict.txt`) | — | — |
+| `lat_dict.txt` | 836-char Latin dictionary (PaddleOCR `ppocrv5_latin_dict.txt`) | — | — |
 
 **Full ZH/KO sign pipeline ≈ 2.2 ms on Hexagon NPU. 100% NPU — zero CPU fallback.**
 
@@ -50,7 +51,9 @@ Measured via Qualcomm AI Hub cloud-hosted real device. Bench date: 2026-06-06.
 | ko rec | **0.49** | 223/223 (100%) | 86× faster |
 | lat rec | **0.31** | 223/223 (100%) | 148× faster |
 
-AI Hub job IDs (verifiable): det=`jg98xw1qp`, zh=`jp38we2x5`, ko=`jgnx7evm5`, lat=`jgj1wln8g`
+AI Hub profile job IDs: det=`jgkd9yowp`, zh=`j56vd1r6p`, ko=`jpv49w9kp`, lat=`jgj1wlwvg`
+(viewable with a Qualcomm AI Hub account at `workbench.aihub.qualcomm.com/jobs/<id>` — the
+job pages require sign-in, they are not publicly browsable without one).
 
 ### Accuracy — public dataset (ReCTS, Apache-2.0)
 Real store-front signage photos (200 scored), standard ChineseOCRBench-style substring matching.
@@ -64,6 +67,11 @@ ReCTS is VQA-style (ground truth = one region; full-image OCR is substring-match
 recall@full understates pure recognition; char-recall reflects character-level quality on
 real, cluttered, perspective-distorted signs.
 
+> **Note on this accuracy number:** measured on the float32 ONNX reference implementation
+> (det + zh rec via `rapidocr-onnxruntime`), not independently re-measured on the int8 TFLite
+> artifacts shipped in this repo. Int8 post-training quantization can shift accuracy from the
+> float baseline — treat this as directional for the shipped models, not an exact figure for them.
+
 > **Vietnamese:** PP-OCRv5's Latin dictionary has **no precomposed Vietnamese tone-mark
 > vowels** (verified by byte-level grep). **Do not use this for Vietnamese** — use a
 > Vietnamese-specialized recognizer (e.g. VietOCR) instead.