While humans may still be able to easily “detect” and read the text, our algorithms will struggle. Non-planar objects: Consider what happens when you wrap text around a bottle - the text on the surface becomes distorted and deformed. Text in natural scenes may be reflective, including logos, signs, etc. Non-paper objects: Most, but not all, paper is not reflective (at least in context of paper you are trying to scan).Resolution: Not all cameras are created equal - we may be dealing with cameras with sub-par resolution.It may be near dark, the flash on the camera may be on, or the sun may be shining brightly, saturating the entire image. Lighting conditions: We cannot make any assumptions regarding our lighting conditions in natural scene images.Blurring: Uncontrolled environments tend to have blur, especially if the end user is utilizing a smartphone that does not have some form of stabilization.Viewing angles: Natural scene text can naturally have viewing angles that are not parallel to the text, making the text harder to recognize.Additionally, low-priced cameras will typically interpolate the pixels of raw sensors to produce real colors. Image/sensor noise: Sensor noise from a handheld camera is typically higher than that of a traditional scanner. I’ve included a summarized version of the natural scene text detection challenges described by Celine Mancas-Thillou and Bernard Gosselin in their excellent 2017 paper, Natural Scene Text Understanding below: Natural scene text detection is different though - and much more challenging.ĭue to the proliferation of cheap digital cameras, and not to mention the fact that nearly every smartphone now has a camera, we need to be highly concerned with the conditions the image was captured under - and furthermore, what assumptions we can and cannot make. An example of such a heuristic-based text detector can be seen in my previous blog post on Detecting machine-readable zones in passport images. Related Work Generic text cleaning packagesįull-blown NLP libraries with some text cleaningīuilt upon the work by Burton DeWilde for Textacy.Figure 1: Examples of natural scene images where text detection is challenging due to lighting conditions, image quality, and non-planar objects (Figure 1 of Mancas-Thillou and Gosselin).ĭetecting text in constrained, controlled environments can typically be accomplished by using heuristic-based approaches, such as exploiting gradient information or the fact that text is typically grouped into paragraphs and characters appear on a straight line. If you don't like the output of clean-text, consider adding a test with your specific input and desired output. Pull requests are especially welcomed when they fix bugs or improve the code quality. If you have a question, found a bug or want to propose a new feature, have a look at the issues page. sklearn import CleanTransformer cleaner = CleanTransformer( no_punct = False, lower = False)Ĭleaner. There is also scikit-learn compatible API to use in your pipelines.Īll of the parameters above work here as well.įrom cleantext. If you need some special handling for your language, feel free to contribute. It should work for the majority of western languages. So far, only English and German are fully supported. For this, take a look at the source code. You may also only use specific functions for cleaning. Lang = "en" # set to 'de' for German special handlingĬarefully choose the arguments that fit your task. From cleantext import clean clean( "some input",įix_unicode = True, # fix various unicode errors to_ascii = True, # transliterate to closest ASCII representation lower = True, # lowercase text no_line_breaks = False, # fully strip line breaks as opposed to only normalizing them no_urls = False, # replace all URLs with a special token no_emails = False, # replace all email addresses with a special token no_phone_numbers = False, # replace all phone numbers with a special token no_numbers = False, # replace all numbers with a special token no_digits = False, # replace all digits with a special token no_currency_symbols = False, # replace all currency symbols with a special token no_punct = False, # remove punctuations replace_with_punct = "", # instead of removing punctuations you may replace them replace_with_url = "",
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