diff --git a/docs/source/using_doctr/using_models.rst b/docs/source/using_doctr/using_models.rst
index aa4d093bb2..10ce23a9f8 100644
--- a/docs/source/using_doctr/using_models.rst
+++ b/docs/source/using_doctr/using_models.rst
@@ -133,7 +133,7 @@ For a comprehensive comparison, we have compiled a detailed benchmark on publicl
 +-----------------------------------------------------------------------------------+----------------------------+----------------------------+--------------------+
 |                                                                                   |        FUNSD               |        CORD                |                    |
 +================+=================================+=================+==============+============+===============+============+===============+====================+
-| **Backend**    | **Architecture**                | **Input shape** | **# params** | **Exact**  | **Partial**   | **Exact**  | **Partial**   | **sec/it (B: 1)**  |
+| **Backend**    | **Architecture**                | **Input shape** | **# params** | **Exact**  | **Partial**   | **Exact**  | **Partial**   | **sec/it (B: 64)** |
 +----------------+---------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+
 | TensorFlow     | crnn_vgg16_bn                   | (32, 128, 3)    | 15.8 M       | 88.12      | 88.85         | 94.68      | 95.10         | 0.9                |
 +----------------+---------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+
@@ -141,7 +141,7 @@ For a comprehensive comparison, we have compiled a detailed benchmark on publicl
 +----------------+---------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+
 | TensorFlow     | crnn_mobilenet_v3_large         | (32, 128, 3)    | 4.5 M        |            |               |            |               | 0.34               |
 +----------------+---------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+
-| Tensorflow     | master                          | (32, 128, 3)    | 58.8 M       |            |               |            |               | 22.3               |
+| Tensorflow     | master                          | (32, 128, 3)    | 58.8 M       | 87.44      | 88.21         | 93.83      | 94.25         | 22.3               |
 +----------------+---------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+
 | TensorFlow     | sar_resnet31                    | (32, 128, 3)    | 57.2 M       |            |               |            |               | 7.1                |
 +----------------+---------------------------------+-----------------+--------------+------------+---------------+------------+---------------+--------------------+
@@ -183,7 +183,7 @@ While most of our recognition models were trained on our french vocab (cf. :ref:
 
 *Disclaimer: both FUNSD subsets combine have 30595 word-level crops which might not be representative enough of the model capabilities*
 
-Seconds per iteration (with a batch size of 1) is computed after a warmup phase of 100 tensors, by measuring the average number of processed tensors per second over 1000 samples. Those results were obtained on a `11th Gen Intel(R) Core(TM) i7-11800H @ 2.30GHz`.
+Seconds per iteration (with a batch size of 64) is computed after a warmup phase of 100 tensors, by measuring the average number of processed tensors per second over 1000 samples. Those results were obtained on a `11th Gen Intel(R) Core(TM) i7-11800H @ 2.30GHz`.
 
 
 Recognition predictors