Using transfer learning to recognize handwriting can be a good approach to digitize your collection of handwritten species labels. Pretrained models like Convolutional Neural Networks (CNNs) trained on large datasets can be fine-tuned to recognize specific handwriting styles.

However, it is important to keep in mind that fine-tuning a model for handwriting recognition can be challenging because of the variability in handwriting styles, especially when dealing with older handwritten labels. You might need to pre-process the images by normalizing the size, orientation, and contrast of the labels before feeding them to the model. Additionally, you may need to manually correct some of the labels after digitization to ensure accuracy.

Another approach you could consider is using Optical Character Recognition (OCR) software. OCR software is designed to recognize text from images, including handwritten text. While OCR may not be as accurate as using a trained model, it can still be an effective solution for digitizing handwritten text. There are many OCR software options available, both free and paid, that you can explore to find one that fits your needs.

Whichever approach you choose, it is important to ensure the preservation of the original labels after digitization. Consider using a high-resolution scanner or camera to capture the labels, and store them in a secure location to ensure their longevity. Additionally, consider backing up your digital data in multiple locations to ensure its availability in the future.
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