Optical Character Recognition (OCR) is a powerful technology that enables machines to recognize and extract text from images or scanned documents. OCR finds applications in various fields, including document digitization, text extraction from images, and text-based data analysis. In this article, we will explore how to use PaddleOCR, an advanced OCR toolkit based on deep learning, for text detection and recognition tasks. We will walk through a code snippet that demonstrates the process step-by-step.
Before we dive into the code, let's ensure we have everything set up to run the PaddleOCR library. Make sure you have the following prerequisites installed on your machine: Python (3.6 or higher) PaddleOCR library Other necessary dependencies (e.g., NumPy, pandas, etc)
Before running the code snippet, make sure you have the necessary libraries installed. You can find all the required packages and their versions in the requirements.txt file
pip install -r requirements.txtIn the provided main.py, we have an example usage of the RecMain class for performing text recognition (OCR) on a folder of images and generating an output Excel file with evaluation metrics:
# Example usage: Replace with your image folder path, label file, and desired output file name
image_folder = "path/to/image_folder"
label_file = "path/to/txt_file"
output_file = "raw_output.xlsx"
if __name__ == "__main__":
RecMain(image_folder=image_folder, rec_file=label_file, output_file=output_file).run_rec()
same can be initiated for detection
#Dec
# Example usage: Replace with your image folder path, label file, and desired output file name
image_folder = "path/to/image_folder"
label_file = "path/to/txt_file"
output_file = "raw_output.xlsx"
if __name__ == "__main__":
DecMain(image_folder_path=image_folder, label_file_path=label_file, output_file=output_file) \
.run_dec()
The code provided is a part of a class named DecMain, which seems to be designed for Optical Character Recognition (OCR) evaluation using ground truth data. It appears to use PaddleOCR to extract text from images and then calculates metrics like precision, recall, and Character Error Rate (CER) to evaluate the performance of the OCR system.
class DecMain:
def __init__(self, image_folder_path, label_file_path, output_file):
self.image_folder_path = image_folder_path
self.label_file_path = label_file_path
self.output_file = output_file
def run_dec(self):
# Check and update the ground truth file
CheckAndUpdateGroundTruth(self.label_file_path).check_and_update_ground_truth_file()
df = OcrToDf(image_folder=self.image_folder_path, label_file=self.label_file_path, det=True, rec=True, cls=False).ocr_to_df()
ground_truth_data = ReadGroundTruthFile(self.label_file_path).read_ground_truth_file()
# Get the extracted text as a list of dictionaries (representing the OCR results)
ocr_results = df.to_dict(orient="records")
# Calculate precision, recall, and CER
precision, recall, total_samples = CalculateMetrics(ground_truth_data, ocr_results).calculate_precision_recall()
CreateSheet(dataframe=df, precision=precision, recall=recall, total_samples=total_samples,
file_name=self.output_file).create_sheet()To perform OCR evaluation using the DecMain class and the provided code, it's crucial to format the ground truth label file correctly.
The label file should be in JSON format and follow the structure as shown below:
image_name.jpg [{"transcription": "215mm 18", "points": [[199, 6], [357, 6], [357, 33], [199, 33]], "difficult": False, "key_cls": "digits"}, {"transcription": "XZE SA", "points": [[15, 6], [140, 6], [140, 36], [15, 36]], "difficult": False, "key_cls": "text"}]
The label file should be in JSON format.
Each line of the file represents an image's OCR ground truth.
Each line contains the filename of the image, followed by the OCR results for that image in the form of a JSON object.
The JSON object should have the following keys:
"transcription": The ground truth text transcription of the image.
"points": A list of four points representing the bounding box coordinates of the text region in the image.
"difficult": A boolean value indicating whether the text region is difficult to recognize.
"key_cls": The class label of the OCR result, e.g., "digits" or "text".
Make sure to follow this format while creating the ground truth label file for accurate OCR evaluation.
The code provided defines a class named RecMain, which is designed to run text recognition (OCR) using a pre-trained OCR model on a folder of images and generate an evaluation Excel sheet.
class RecMain:
def __init__(self, image_folder, rec_file, output_file):
self.image_folder = image_folder
self.rec_file = rec_file
self.output_file = output_file
def run_rec(self):
image_paths = GetImagePathsFromFolder(self.image_folder, self.rec_file). \
get_image_paths_from_folder()
ocr_model = LoadRecModel().load_model()
results = ProcessImages(ocr=ocr_model, image_paths=image_paths).process_images()
ground_truth_data = ConvertTextToDict(self.rec_file).convert_txt_to_dict()
model_predictions, ground_truth_texts, image_names, precision, recall, \
overall_model_precision, overall_model_recall, cer_data_list = EvaluateRecModel(results,
ground_truth_data).evaluate_model()
# Create Excel sheet
CreateMetricExcel(image_names, model_predictions, ground_truth_texts,
precision, recall, cer_data_list, overall_model_precision, overall_model_recall,
self.output_file).create_excel_sheet()To perform OCR evaluation using the RecMain class and the provided code, it's essential to format the ground truth (GT) text file correctly.
The GT text file should be in the following format:
image_name.jpg text
Each line of the file represents an image's GT text.
Each line contains the filename of the image, followed by a tab character (\t), and then the GT text for that image.
Ensure that the GT text file contains GT text entries for all the images present in the image folder specified in the RecMain class. The GT text should match the actual text content present in the images. This format is necessary for accurate evaluation of the OCR model's performance.
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Copyright [2023] [Vinod Baste]
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