Recognizing Handwritten Characters
نویسنده
چکیده
Traditional CNN-based text recognition problems have either been single-character classification like MNIST or complex, stateful full-word classifications like the LSTM approach in Graves et al. I explore a stateless approach to word recognition by using Faster R-CNN’s region proposal network in conjunction with a heuristic to produce word labels from region proposals. While the results are promising for word images that have characters with very little overlap, they fall short for cursive word images, where individual characters require surrounding context to be identified.
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Machine Recognition of Hand Written Characters using Neural Networks
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