How does tesseract ocr work

We can improve our Tesseract text detection results simply by supplying a --min-conf value:. I strongly believe that if you had the right teacher you could master computer vision and deep learning. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated?

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Join me in computer vision mastery. Click here to join PyImageSearch University. Whenever confronted with an OCR project, be sure to apply both methods and see which method gives you the best results — let your empirical results guide you.

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Click here to download the source code to this post. Looking for the source code to this post? Figure 1: Tesseract can be used for both text localization and text detection. Text localization can be thought of as a specialized form of object detection. We are now ready to implement text detection and localization with Tesseract.

You can install the python wrapper for tesseract after this using pip. Tesseract library is shipped with a handy command-line tool called tesseract.

We can use this tool to perform OCR on images and the output is stored in a text file. To specify the language model name, write language shortcut after -l flag, by default it takes English language:. By default, Tesseract expects a page of text when it segments an image.

If you're just seeking to OCR a small region, try a different segmentation mode, using the --psm argument. There are 14 modes available which can be found here. By default, Tesseract fully automates the page segmentation but does not perform orientation and script detection. To specify the parameter, type the following:. There is also one more important argument, OCR engine mode oem. There are four modes of operation chosen using the --oem option. It is also useful as a stand-alone invocation script to tesseract, as it can read all image types supported by the Pillow and Leptonica imaging libraries, including jpeg, png, gif, bmp, tiff, and others.

More info about Python approach read here. The code for this tutorial can be found in this repository. To avoid all the ways your tesseract output accuracy can drop, you need to make sure the image is appropriately pre-processed. Using Pytesseract, you can get the bounding box information for your OCR results using the following code. The script below will give you bounding box information for each character detected by tesseract during OCR.

Want to digitize invoices, PDFs or number plates? Head over to Nanonets and build OCR models for free! Using this dictionary, we can get each word detected, their bounding box information, the text in them and the confidence scores for each. Take the example of trying to find where a date is in an image.

Here our template will be a regular expression pattern that we will match with our OCR results to find the appropriate bounding boxes. There are several ways a page of text can be analysed. The tesseract api provides several page segmentation modes if you want to run OCR on only a small region or in different orientations, etc.

Default 4 Assume a single column of text of variable sizes. Find as much text as possible in no particular order. Treat the image as a single text line, bypassing hacks that are Tesseract-specific. To change your page segmentation mode, change the --psm argument in your custom config string to any of the above mentioned mode codes. You can detect the orientation of text in your image and also the script in which it is written. The following image - after running through the following code -.

Take this image for example - The text extracted from this image looks like this. Say you only want to detect certain characters from the given image and ignore the rest. You can specify your whitelist of characters here, we have used all the lowercase characters from a to z only by using the following config. If you are sure some characters or expressions definitely will not turn up in your text the OCR will return wrong text in place of blacklisted characters otherwise , you can blacklist those characters by using the following config.

You can find out the LANG values here. You can download the. Note - Only languages that have a. Take this image for example - You can work with multiple languages by changing the LANG parameter as such -. Note - The language specified first to the -l parameter is the primary language.

Unfortunately tesseract does not have a feature to detect language of the text in an image automatically. An alternative solution is provided by another python module called langdetect which can be installed via pip.

This module again, does not detect the language of text using an image but needs string input to detect the language from. The best way to do this is by first using tesseract to get OCR text in whatever languages you might feel are in there, using langdetect to find what languages are included in the OCR text and then run OCR again with the languages found. The language codes used by langdetect follow ISO codes.

To compare, please check this and this. We find that the language used in the text are english and spanish instead. Note - Tesseract performs badly when, in an image with multiple languages, the languages specified in the config are wrong or aren't mentioned at all. This can mislead the langdetect module quite a bit as well.

Need to digitize documents, receipts or invoices but too lazy to code? Neural networks require significantly more training data and train a lot slower than base Tesseract.

For Latin-based languages, the existing model data provided has been trained on about text lines spanning about fonts. In order to successfully run the Tesseract 4. Visit github repo for files and tools. Even with all these new training data, therefore here are few options for training:. A guide on how to train on your custom data and create. Tesseract works best when there is a clean segmentation of the foreground text from the background. In practice, it can be extremely challenging to guarantee these types of setup.

There are a variety of reasons you might not get good quality output from Tesseract like if the image has noise on the background. The better the image quality size, contrast, lightning the better the recognition result.

It requires a bit of preprocessing to improve the OCR results, images need to be scaled appropriately, have as much image contrast as possible, and the text must be horizontally aligned.

Tesseract OCR is quite powerful but does have the following limitations. You do not have to worry about pre-processing your images or worry about matching templates or build rule based engines to increase the accuracy of your OCR model.

You can upload your data, annotate it, set the model to train and wait for getting predictions through a browser based UI without writing a single line of code, worrying about GPUs or finding the right architectures for your deep learning models. You can also acquire the JSON responses of each prediction to integrate it with your own systems and build machine learning powered apps built on state of the art algorithms and a strong infrastructure.

Step 6: Upload the Training Data The training data is found in images image files and annotations annotations for the image files. You will get an email once the model is trained. In the meanwhile you check the state of the model. Step 9: Make Prediction Once the model is trained. You can make predictions using the model.

All the fields are structured into an easy to use GUI which allows the user to take advantage of the OCR technology and assist in making it better as they go, without having to type any code or understand how the technology works.