License plate detection and character recognition pose challenges due  to environmental 
sensitivity, such as lighting, dust, and the impact of the chosen font type on recognition 
tasks.  Automatic  License  Plate  Detection  and  Recognition  (ALPR)  are  crucial 
in  practical  applications  such  as  traffic  control  and  parking,  vehicle  tracking,  toll 
collection,  and  law  enforcement.  While  much  research  has  been  done  using  image 
processing  and  machine  learning  algorithms,  deep  learning  methods  need  further 
exploration  due  to  their  recent  advances  in  reliable  performance  in  various  scenarios. 
Moreover, current proposals are limited to specific regions and dataset applicability. 
This  study  has  a  dual  focus:  firstly, we  suggest  utilizing  a Deep  Learning  technique, 
specifically  using Faster R-CNN  for  the  license  plate  detection  task  and  a CNN-RNN 
model  with  CTC  loss,  and  a MobileNet  V3  backbone  for  recognition  task. We  also 
utilized You Only Look Once (YOLO) for license plate detection and recognition tasks. 
Secondly, we aim to assess font features within the LP context. This work uses Brazilian 
dataset and datasets from  two different provinces  in Canada and two different states  in 
the  United  States  of America,  including  Ontario,  Quebec,  California,  and  New York 
State. We  suggest  employing  an  adaptive  algorithm  based  on Faster R-CNN  and CTC 
network  along  with  YOLO,  fine-tuned  with  optimized  parameters  to  improve  its 
effectiveness  using  two  different  approaches,  including  domain  generalization. 
Alongside  presenting  the  recall  ratio  findings,  this  study will perform a  thorough error 
analysis  to  gain  insights  into  the  nature  of  false  positives.  The  proposed  model 
demonstrated a commendable recall ratio of 94% using a single YOLO network. 
Specific  fonts  pose  readability  challenges  for humans, while  others  present  difficulties 
for  computer  systems  regarding  recognition.  In  this  study,  we  provide  five  sets  of 
outcomes  for  font  assessment:  results  about  font  anatomy  and  those  related  to  the 
recognition of commercial products. The font anatomy analysis focuses on five specific 
fonts: Driver Gothic, Dreadnought, California Clarendon, Zurich Extra Condensed, and 
Mandatory. Additionally, we assess  the  impact of these fonts  in the context of a dataset 
made of five different  license plates using a commercial product, OpenALPR. The font 
anatomy findings unveil significant confusion cases and quality features associated with 
chosen fonts.