From e1ba9c7d0f2cb9456a3dc2e1694368e8a6266f71 Mon Sep 17 00:00:00 2001
From: ankandrew <61120139+ankandrew@users.noreply.github.com>
Date: Fri, 10 May 2024 19:04:00 -0300
Subject: [PATCH] Minor text correction

---
 README.md     | 8 ++++----
 docs/index.md | 8 ++++----
 2 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index b195047..94d33fd 100644
--- a/README.md
+++ b/README.md
@@ -31,10 +31,10 @@ The idea is to use this after a plate object detector, since the OCR expects the
 
 ### Available Models
 
-|              Model Name              | Time b=1<br/> (ms)<sup>[1]</sup> | Throughput <br/> (plates/second)<sup>[1]</sup> |                                                              Dataset                                                               | Accuracy<sup>[2]</sup> |                     Dataset                     |
-|:------------------------------------:|:--------------------------------:|:----------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------:|:----------------------:|:-----------------------------------------------:|
-|    `argentinian-plates-cnn-model`    |               2.1                |                      476                       |      [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_plate_dataset.zip)       |         94.05%         |        Non-synthetic, plates up to 2020.        |
-| `argentinian-plates-cnn-synth-model` |               2.1                |                      476                       | [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_plate_dataset_plus_synth.zip) |         94.19%         | Plates up to 2020 + Bike & car synthetic plates |
+|              Model Name              | Time b=1<br/> (ms)<sup>[1]</sup> | Throughput <br/> (plates/second)<sup>[1]</sup> |                                                              Dataset                                                               | Accuracy<sup>[2]</sup> |                Dataset                |
+|:------------------------------------:|:--------------------------------:|:----------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------:|:----------------------:|:-------------------------------------:|
+|    `argentinian-plates-cnn-model`    |               2.1                |                      476                       |      [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_plate_dataset.zip)       |         94.05%         |   Non-synthetic, plates up to 2020.   |
+| `argentinian-plates-cnn-synth-model` |               2.1                |                      476                       | [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_plate_dataset_plus_synth.zip) |         94.19%         | Plates up to 2020 + synthetic plates. |
 
 _<sup>[1]</sup> Inference on Mac M1 chip using CPUExecutionProvider. Utilizing CoreMLExecutionProvider accelerates speed
 by 5x._
diff --git a/docs/index.md b/docs/index.md
index e780621..2d0f01e 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -20,10 +20,10 @@ The idea is to use this after a plate object detector, since the OCR expects the
 
 We currently have the following available models:
 
-|              Model Name              | Time b=1<br/> (ms)<sup>[1]</sup> | Throughput <br/> (plates/second)<sup>[1]</sup> |                                                              Dataset                                                               | Accuracy<sup>[2]</sup> |                     Dataset                     |
-|:------------------------------------:|:--------------------------------:|:----------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------:|:----------------------:|:-----------------------------------------------:|
-|    `argentinian-plates-cnn-model`    |               2.1                |                      476                       |      [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_plate_dataset.zip)       |         94.05%         |        Non-synthetic, plates up to 2020.        |
-| `argentinian-plates-cnn-synth-model` |               2.1                |                      476                       | [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_plate_dataset_plus_synth.zip) |         94.19%         | Plates up to 2020 + Bike & car synthetic plates |
+|              Model Name              | Time b=1<br/> (ms)<sup>[1]</sup> | Throughput <br/> (plates/second)<sup>[1]</sup> |                                                              Dataset                                                               | Accuracy<sup>[2]</sup> |                Dataset                |
+|:------------------------------------:|:--------------------------------:|:----------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------:|:----------------------:|:-------------------------------------:|
+|    `argentinian-plates-cnn-model`    |               2.1                |                      476                       |      [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_plate_dataset.zip)       |         94.05%         |   Non-synthetic, plates up to 2020.   |
+| `argentinian-plates-cnn-synth-model` |               2.1                |                      476                       | [arg_plate_dataset.zip](https://github.com/ankandrew/fast-plate-ocr/releases/download/arg-plates/arg_plate_dataset_plus_synth.zip) |         94.19%         | Plates up to 2020 + synthetic plates. |
 
 _<sup>[1]</sup> Inference on Mac M1 chip using CPUExecutionProvider. Utilizing CoreMLExecutionProvider accelerates speed
 by 5x._