shiredude95
Joined 8 years ago
Joined 8 years ago
This scene from the movie inception rings true in this case: https://www.youtube.com/watch?v=UsYc5tCk1t8
We were basically trying to transcribe message screenshots which should have been relatively straightforward given the homogeneity of the font. But this was not the case as tesseract was not trained in the layout of msg screenshots. The accuracy of raw tesseract on our test dataset was somehwere about 0.5-0.6 BLEU.
Once we were able to isolate individual parts of the image and feed it to tesseract, we were able to get around 0.9 BLEU on the same dataset.
TLDR;Some nifty image processing is required to make tesseract perform as expected.
[0] (https://www.askgoose.com) [1] (https://github.com/tesseract-ocr/tesseract)
You can improve contextual summarization by splitting the x sentences into x/n buckets. Then based on the percent of article to be summarized (eg return 60% of the article), pick the sentences ranked in the top 60% of each bucket. Then do this for all the x sentences, ie top 60% across buckets, and combine them together.
This prevents the bias rising from picking a sentence with a lot of critical words.