Yes, that's what I'm doing. After I reduced the image size and increased the image contrast and brightness, tesseract was able to recognize about 5 characters. But still, it is hard to recognize the whole string.
Anyone has another approach I could try? Thank you. On Friday, March 4, 2016 at 3:04:03 AM UTC-3, Meh Hem wrote: > > If I was going to attempt this I would attempt to solve this via > pre-processing. Shouldn't be too difficult to pre process to remove the > white spaces in the chars to create consistent shapes that tesseract could > read easily. > > Could possibly need some up-scaling to off set the reduced size too. > > I don't think this is the answer you are after, but getting tesseract to > consider broken shapes as blobs will be tedious. > > > On Friday, March 4, 2016 at 2:23:34 AM UTC+8, Roger wrote: >> >> Does running tesseract training exhaustive on the .box and .tif files, >> helps in the recognition accuracy increase? >> >> On Wednesday, March 2, 2016 at 4:23:44 AM UTC-3, Roger wrote: >>> >>> I am training tesseract to recognize CMC7 font, following this >>> <http://michaeljaylissner.com/posts/2012/02/11/adding-new-fonts-to-tesseract-3-ocr-engine/> >>> and this >>> <https://github.com/tesseract-ocr/tesseract/wiki/TrainingTesseract> >>> tutorial. >>> >>> >>> I have made a .tif file with 2621 characters, and created the .box file, >>> going into every character to make sure the X and Y positions are correct >>> (the rectangle around the character). >>> >>> >>> After that, I have run the command to train tesseract: >>> >>> >>> tesseract por.cmc7.exp0.tif por.cmc7.box nobatch box.train .stderr >>> >>> >>> I've made a shell script that calls this command in a loop, so the >>> training wil be repeated a bunch of times. However, after a bunch of: >>> >>> APLY_BOXES: Unlabelled word at :Bounding box=(762,2763)->(783,2776) >>> >>> APPLY_BOXES: Unlabelled word at :Bounding box=(774,2269)->(783,2277) >>> >>> APPLY_BOXES: Unlabelled word at :Bounding box=(787,2269)->(789,2277) ... >>> >>> >>> >>> The result is always: >>> >>> Found 420 good blobs. >>> >>> 2129 remaining unlabelled words deleted. >>> >>> Generated training data for 420 words >>> >>> It is running for several hours, and still it generated training data >>> for only 420 words. And after I run tesseract on a check image to test it >>> will recognize the characters, it doesn't work (doesn't recognize the >>> characters and return random letters and symbols). >>> >>> >>> How can I make it recognize all the characters in the .tif image? >>> >>> >>> Thank you. >>> >>> I have attached the .box and .tif in the zip file. >>> >> -- You received this message because you are subscribed to the Google Groups "tesseract-ocr" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at https://groups.google.com/group/tesseract-ocr. To view this discussion on the web visit https://groups.google.com/d/msgid/tesseract-ocr/b5e1043b-45fd-468f-bcfb-303f56b4fb8b%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
