Using transfer learning to recognize handwriting is a promising approach. Transfer learning refers to training an existing handwriting recognition model, which was designed to transcribe documents from a previous data set, to recognize documents from a new set. This allows new models to utilize previously learned knowledge on new untranscribed data. This approach has been used successfully in various applications of handwriting recognition.

Another approach that you may consider is active transfer learning, which combines both active and transfer learning into a unified framework. This approach helps to overcome the weaknesses of both transferred models and models obtained through active learning.

Do you have any specific questions about these approaches?
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