| dc.description.abstract | This study investigates a rapid recognition method for extracting text content from cells in specific engineering chart images using morphology and optical character recognition (OCR) techniques and recording the results. The research is applicable to specific engineering chart images, and if it needs to be applied to different types of engineering chart images, the parameters of the corresponding engineering chart image rules can be modified.
Python programming language serves as the foundation for this research. In the preprocessing stage, the Otsu thresholding method is utilized for image binarization, and morphology operations are employed to extract the positions of cells in specific engineering chart images. In the text recognition process, the Tesseract-OCR package is used and divided into three stages for text recognition and extraction: 1. automatic page segmentation with a pre-trained English model, 2. word segmentation with a retrained English model, and 3. character segmentation with a retrained English model. Finally, regular expressions combined with an exhaustive approach are used to correct errors and content that deviate from the rules.
The experimental results indicate that although the Tesseract-OCR package provides users with a pre-trained English model, which exhibits excellent recognition capabilities for long strings, it tends to generate errors in recognizing words or individual characters within cells. Using the three-stage approach with the pre-trained English model, the recognition accuracy is only 14.65%. However, by retraining the English model using a dataset created from specific engineering chart images, the recognition capability for words or individual characters within cells improves, achieving an accuracy of 58.04%. In the post-processing stage, by listing all errors and content that deviate from the rules based on specific engineering chart rules and replacing them with correct characters, the accuracy can be enhanced to 100%. | en_US |