The factors that affect the OCR recognition rate mainly include the following: image quality: the clarity of the image has a significant impact on the OCR recognition rate.

If the image is blurred or the resolution is low, the recognition rate will be greatly reduced.

The lighting conditions and shooting Angle of the image are also important.

Images with uniform lighting and a positive Angle are easier to identify accurately.

Writing quality and style: The quality of handwriting will directly affect the recognition rate.

Clear, regular handwriting helps to improve the recognition rate, while vague, sloppy handwriting may lead to recognition failure.

Each person's writing style is different, and some special writing styles (such as linking characters, artistic characters) may cause difficulties in recognition.

Font, size, and shape: Different fonts, sizes, and shapes also affect the recognition rate of OCR.

Some special fonts, small font size characters, or unconventional font shapes can cause misidentification.

Background and noise: The background complexity of the image affects the recognition rate.

If the background is cluttered or there is pattern interference, it may reduce the recognition ability of the OCR system.

Noise is also an important factor, and too much noise can interfere with the text recognition process.

Character spacing and line spacing: Reasonable character spacing and line spacing is conducive to improving recognition accuracy.

Overcrowding or sparse spacing can lead to recognition errors.

Languages and character sets: OCR systems are often designed and trained for specific languages and character sets.

For non-standard character sets or handwritten text in non-mainstream languages, the recognition ability of the OCR system may be limited.

Training data and algorithms: The training data of the OCR system and the algorithms used are critical to the recognition rate.

More training data and advanced algorithms can often improve OCR accuracy.

In summary, OCR recognition rate is affected by multiple factors such as image quality, writing quality and style, font size and shape, background and noise, character spacing and line spacing, language and character set, and training data and algorithms.

In practical application, it is necessary to consider these factors comprehensively and choose the appropriate OCR solution to improve the recognition rate.