Making a Tesseract model for my license plates
Requirements
First download the following repository:git clone https://github.com/tesseract-ocr/tesstrain.git
Navigate to the folder: cd tesstrain
Compile the application:make tesseract-langdata
Make a new to save the default language model to: mkdir -p usr/share/tessdata
Now download the model: wget -P usr/share/tessdata https://github.com/tesseract-ocr/tessdata_best/raw/main/eng.traineddata
Making the dataset
I made pictures of random cars outside for my other model. When you have a decently sized dataset (I used 279 images) make sure to extract the license plates from the images using the data from Label Studio. This is a python script I used:
import os
import cv2
# Define paths
labels_path = 'dataset/licensePlate/yolo/labels/'
images_path = 'dataset/licensePlate/yolo/images'
output_path = 'licenseplates/'
# Make sure output directory exists
os.makedirs(output_path, exist_ok=True)
def extract_license_plate(image_name):
# Load image
img_path = os.path.join(images_path, image_name)
image = cv2.imread(img_path)
height, width, _ = image.shape
# Corresponding label file
label_file_name = f"{os.path.splitext(image_name)[0]}.txt"
label_file_path = os.path.join(labels_path, label_file_name)
if not os.path.exists(label_file_path):
print(f"No label found for {image_name}")
return
with open(label_file_path) as f:
lines = f.readlines()
for line in lines:
parts = line.strip().split()
class_id = int(parts[0])
if class_id == 1:
x_center, y_center, w, h= map(float ,parts [1:] )
x_min=int((x_center-w /2)*width )
y_min=int((y_center-h /2)* height )
x_max=int((x_center+ w /2)*width )
y_max=int((y_center+h /2 )*height )
cropped_img=image[y_min:y_max,x_min:x_max]
# Save the cropped image as a PNG file
png_output_file_name=os.path.join(output_path,f"lp_{os.path.splitext(image_name)[0]}.png")
cv2.imwrite(png_output_file_name,cropped_img)
print(f"Cropped image saved as {png_output_file_name}")
for image_filename in os.listdir(images_path):
if image_filename.endswith(('.jpg','.jpeg','.png')):
extract_license_plate(image_filename)
print("License plate extraction complete.")
NOTE: The images have to be in PNG format. If they aren’t, run this.mogrify -format png *.*
Process the dataset
This step will involve quite a bit of work, sadly. You will need to create a file, a .tg.txt file with the same name as the image. Here is a python script using gradio to make this a bit easier. Please note the image_folder. Point that folder to your dataset.
import gradio as gr
from PIL import Image
import os
image_folder = "licenseplates"
image_files = [os.path.join(image_folder, f) for f in os.listdir(image_folder) if os.path.isfile(os.path.join(image_folder, f))]
current_image_index = 0
def load_image_and_text(evt: gr.SelectData):
global current_image_index, image_files, corrected_output
image_path = os.path.join(image_folder, evt.value["image"]["orig_name"])
current_image_index = image_files.index(image_path)
text = read_text(image_path)
return image_path, text
# Create a Gradio Interface
with gr.Blocks() as app:
# Function to save the corrected text
def save_corrected_text_gr(filename, text):
original_filename = filename.rsplit("/", 1)[-1].rsplit(".", 1)[0]
with open(f"{image_folder}/{original_filename}.gt.txt", "w") as text_file:
text_file.write(text)
def read_text(image_file_path):
# Extract the base filename without the directory path and extension
base_filename = os.path.basename(image_file_path).rsplit(".", 1)[0]
text_file_path = f"{image_folder}/{base_filename}.gt.txt"
# Check if the corresponding .gt.txt file exists
if os.path.exists(text_file_path):
# Read the text from the file
with open(text_file_path, "r") as file:
text = file.read()
else:
# If the file does not exist, set text to an empty string
text = ""
return text
# Update the next_image and previous_next functions to refresh the image component
# Update the next_image and previous_image functions to use read_text
def next_image():
global current_image_index, image_display, image_files, corrected_output
if current_image_index < len(image_files) - 1:
current_image_index += 1
n_image = image_files[current_image_index]
# Read text from corresponding text file
text = read_text(n_image)
return (n_image, text)
def previous_image():
global current_image_index, image_display, image_files, corrected_output
if current_image_index > 0:
current_image_index -= 1
p_image = image_files[current_image_index]
# Read text from corresponding text file
text = read_text(p_image)
return (p_image, text)
with gr.Row():
with gr.Column(scale=1):
image_list = gr.Gallery(label="Image Gallery", value=image_files, allow_preview=False)
with gr.Column(scale=4):
current_image_file = gr.Textbox(image_files[current_image_index])
image_display = gr.Image(current_image_file.value)
current_image_file.change(fn=lambda o: o, inputs=[current_image_file], outputs=[image_display])
corrected_output = gr.Textbox(label="Corrected Text", interactive=True)
# Create the buttons to go to the next and previous image
next_button = gr.Button("Next Image").click(fn=next_image, inputs=[], outputs=[current_image_file, corrected_output])
previous_button = gr.Button("Previous Image").click(fn=previous_image, inputs=[], outputs=[current_image_file, corrected_output])
# Create the button to save the corrected text
save_button = gr.Button("Save Corrected Text").click(
fn=save_corrected_text_gr,
inputs=[current_image_file, corrected_output],
outputs=[]
)
image_list.select(load_image_and_text, outputs=[current_image_file, corrected_output])
# Launch the interface
app.launch()It will look like this.
Train the model
First navigate to the tesstrain folder you cloned before:cd tesstrain
Now copy your dataset to the required folder:cp -r {LOCATION_OF_DATASET}/* data/licenseplates-ground-truth
Train the model:make training MODEL_NAME=licenseplates START_MODEL=eng FINETUNE_TYPE=Impact
Move the new model to the model folder:sudo cp data/licenseplates.traineddata /usr/share/tessdata
Using the model
To use the new model:tesseract data/licenseplates-ground-truth/lp_5c3f36e7-20240601_194130.png output -l licenseplates
NOTE: The image you are going to run this on need to be processed by this script:
import cv2
import numpy as np
from scipy.ndimage import interpolation as inter
import os
def rotate_image(image, angle):
(h, w) = image.shape[: 2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
corrected = cv2.warpAffine(image, M, (w, h), flags = cv2.INTER_CUBIC, \
borderMode = cv2.BORDER_REPLICATE)
return corrected
def determine_score(arr):
histogram = np.sum(arr, axis = 2, dtype = float)
score = np.sum((histogram[..., 1 :] - histogram[..., : -1]) ** 2, \
axis = 1, dtype = float)
return score
def correct_skew(image, delta = 0.1, limit = 20):
thresh = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
angles = np.arange(-limit, limit + delta, delta)
img_stack = np.stack([rotate_image(thresh, angle) for angle in angles], axis = 0)
scores = determine_score(img_stack)
best_angle = angles[np.argmax(scores)]
corrected = rotate_image(image, best_angle)
return best_angle, corrected
def correct_image(image):
# treshold color
outfile = cv2.threshold(corrected, 66, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]
# invert image
return (255-outfile)
image_folder = 'lp'
output_folder = 'lp2'
if not os.path.exists(output_folder):
os.makedirs(output_folder)
for filename in os.listdir(image_folder):
if filename.endswith('.png'):
image = cv2.imread(os.path.join(image_folder, filename), 0)
angle, corrected = correct_skew(image)
output_filename = os.path.join(output_folder, filename)
cv2.imwrite(output_filename + ".png", correct_image(corrected))