Research on the effects of image pre -processing on the effects of the "Pytesseract" library recognition effect

Research on the effects of image pre -processing on the effects of the "Pytesseract" library recognition effect Abstract: With the rapid development of computer vision and optical character recognition, more and more application scenarios require text information from the image.There are some difficulties in identification of Chinese characters, so the image needs to be pre -processed to enhance the accuracy of the Chinese character recognition of the ‘Pytesseract’ library in Chinese character recognition.This study aims to explore the effects of different image pre -processing methods on the identification effect of the "Pytesseract" class library, and provide technical support for the automatic recognition of Chinese characters. 1 Introduction With the continuous improvement of social informationization, a large number of images and documents need to be transformed into editing and searchable text formats.The application of computer vision and optical character recognition technology has certain limitations, especially in the recognition of Chinese characters.‘Pytesseract’ is a Python package based on the Tesseract OCR engine, which is usually applied to text recognition tasks.However, in terms of Chinese character recognition, its accuracy needs to be further improved. 2. Image pre -processing method In order to improve the effect of ‘pytesseract’ in Chinese character recognition, this study tried the following commonly used image pre -processing methods: 2.1 dual value Translate the image into a two -value image to improve the contrast between text and background. 2.2 sharpness Enhance the edge characteristics of text through image sharpness algorithms to improve the accuracy of recognition. 2.3 noise reduction Use the noise reduction algorithm to remove the noise in the image and keep text information. 2.4 Comparison enhancement By adjusting the contrast of the image to improve the differences between text and background to improve the identification results. 3. ‘pytesseract’ configuration and program code Before the image processing, you need to correctly configure the "Pytesseract" class library and related dependencies.The specific program code is as follows: python import pytesseract from PIL import Image # The OCR engine and language of the ‘Pytesseract’ pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe' lang = 'chi_sim' # 方法 pre -processing method def image_preprocessing(image): # Convert the image to gray image image = image.convert('L') #Axic pre -processing code (select pre -processing method according to needs) # Binarization # image = image.point(lambda x: 0 if x < 128 else 255, '1') # # image = image.filter(ImageFilter.SHARPEN) # 降 降 # image = image.filter(ImageFilter.DETAIL) # Comparison enhancement # enhancer = ImageEnhance.Contrast(image) # image = enhancer.enhance(2) return image # Read the image and processed pre -processing image_path = 'example.jpg' image = Image.open(image_path) processed_image = image_preprocessing(image) # Use ‘pytesseract’ for text recognition text = pytesseract.image_to_string(processed_image, lang=lang) print(text) 4. Experimental results and analysis This study experimented with ‘pytesseract’ based on different image pre -processing methods. Taking Chinese character as an example, the accuracy of identification is evaluated.The experimental results show that under different application scenarios and specific character characteristics, different image pre -processing methods have different effects on the identification effect of 'Pytesseract'.Therefore, in actual applications, the appropriate image pre -processing method is required according to the actual situation. 5. Conclusion and prospects In this study, the image pre -processing method in Chinese character recognition was experimentally studied for the recognition effect of the "Pytesseract" class library.The results show that the image pre -processing steps have a significant impact on the accuracy of identification.However, there are still some problems that are challenged and to be resolved, such as character recognition in complex backgrounds, identification under uneven light conditions.Future research can further explore these issues and propose more innovative and effective image pre -processing methods to improve the accuracy and robustness of the "Pytesseract" library in Chinese character recognition. references: - Tesseract OCR: https://github.com/tesseract-ocr/tesseract (Note: This article is a sample article generated based on the topic. Some of the content may not be real research results and conclusions. The research methods and experiment results need to be designed and verified according to the actual situation.)