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[Trinh Livingston]

OCROptical Character Recognition. In other words, OCR systems transform a two-dimensional image of text, that could contain machine printed or handwritten text from its image representation into machine-readable text. OCR as a process generally consists of several sub-processes to perform as accurately as possible. The subprocesses are:

For almost two decades, optical character recognition systems have been widely used to provide automated text entry into computerized systems. Yet in all this time, conventional online OCR systems (like zonal OCR) have never overcome their inability to read more than a handful of type fonts and page formats. Proportionally spaced type (which includes virtually all typeset copy), laser printer fonts, and even many non-proportional typewriter fonts, have remained beyond the reach of these systems. And as a result, conventional OCR has never achieved more than a marginal impact on the total number of documents needing conversion into digital form.

Next-generation OCR engines deal with these problems mentioned above really good by utilizing the latest research in the area of deep learning. By leveraging the combination of deep models and huge datasets publicly available, models achieve state-of-the-art accuracies on given tasks. Nowadays it is also possible to generate synthetic data with different fonts using generative adversarial networks and few other generative approaches.

Optical Character Recognition remains a challenging problem when text occurs in unconstrained environments, like natural scenes, due to geometrical distortions, complex backgrounds, and diverse fonts. The technology still holds an immense potential due to the various use-cases of deep learning based OCR like

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There are a lot of optical character recognition software available. I did not find any quality comparison between them, but I will write about some of them that seem to be the most developer-friendly.

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OCRopus - OCRopus is an open-source OCR system allowing easy evaluation and reuse of the OCR components by both researchers and companies. A collection of document analysis programs, not a turn-key OCR system. To apply it to your documents, you may need to do some image preprocessing, and possibly also train new models. In addition to the recognition scripts themselves, there are several scripts for ground truth editing and correction, measuring error rates, and determining confusion matrices that are easy to use and edit.

Ocular - Ocular works best on documents printed using a hand press, including those written in multiple languages. It operates using the command line. It is a state-of-the-art historical OCR system. Its primary features are:

SwiftOCR - I will also mention the OCR engine written in Swift since there is huge development being made into advancing the use of Swift as the development programming language used for deep learning. Check out the blog to find out more why. SwiftOCR is a fast and simple OCR library that uses neural networks for image recognition. SwiftOCR claims that its engine outperforms well known Tessaract library.

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Tesseract is an open-source text recognition (OCR) Engine, available under the Apache 2.0 license. It can be used directly, or (for programmers) using an API to extract printed text from images. It supports a wide variety of languages. Tesseract doesn't have a built-in GUI, but there are several available from the 3rdParty page. Tesseract is compatible with many programming languages and frameworks through wrappers that can be found on Github. It can be used with the existing layout analysis to recognize text within a large document, or it can be used in conjunction with an external text detector to recognize text from an image of a single text line.

Tesseract 4.00 includes a new neural network subsystem configured as a text line recognizer. It has its origins in OCRopus' Python-based LSTM implementation but has been redesigned for Tesseract in C++. The neural network system in Tesseract pre-dates TensorFlow but is compatible with it, as there is a network description language called Variable Graph Specification Language (VGSL), that is also available for TensorFlow.

To recognize an image containing a single character, we typically use a Convolutional Neural Network (CNN). Text of arbitrary length is a sequence of characters, and such problems are solved using RNNs and LSTM is a popular form of RNN. Read this post to learn more about LSTM.

LSTMs are great at learning sequences but slow down a lot when the number of states is too large. There are empirical results that suggest it is better to ask an LSTM to learn a long sequence than a short sequence of many classes. Tesseract developed from OCRopus model in Python which was a fork of a LSMT in C++, called CLSTM. CLSTM is an implementation of the LSTM recurrent neural network model in C++, using the Eigen library for numerical computations.

Word finding was done by organizing text lines into blobs, and the lines and regions are analyzed for fixed pitch or proportional text. Text lines are broken into words differently according to the kind of character spacing. Recognition then proceeds as a two-pass process. In the first pass, an attempt is made to recognize each word in turn. Each word that is satisfactory is passed to an adaptive classifier as training data. The adaptive classifier then gets a chance to more accurately recognize text lower down the page.

Modernization of the Tesseract tool was an effort on code cleaning and adding a new LSTM model. The input image is processed in boxes (rectangle) line by line feeding into the LSTM model and giving output. In the image below we can visualize how it works.

After adding a new training tool and training the model with a lot of data and fonts, Tesseract achieves better performance. Still, not good enough to work on handwritten text and weird fonts. It is possible to fine-tune or retrain top layers for experimentation.

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 on Github. By default, Tesseract fully automates the page segmentation but does not perform orientation and script detection. To specify the parameter, type the following:

Pytesseract is a Python library that serves as a wrapper for Google's Tesseract-OCR Engine. It allows developers to utilize Tesseract's Optical Character Recognition (OCR) capabilities through Python. With Pytesseract, you can easily extract text from images, making it a valuable tool for tasks that require converting image-based text into editable and searchable formats. This library simplifies the integration of OCR functionalities into Python applications, enabling tasks like automated data entry, document digitization, and text recognition from various image formats.

Pytesseract or Python-tesseract is an OCR tool for python that also serves as a wrapper for the Tesseract-OCR Engine. It can read and recognize text in images and is commonly used in python ocr image to text use cases.

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.

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. We will use the regex module and the image_to_data function for this.

0 Orientation and script detection (OSD) only.

1 Automatic page segmentation with OSD.

2 Automatic page segmentation, but no OSD, or OCR.

3 Fully automatic page segmentation, but no OSD. (Default)

4 Assume a single column of text of variable sizes.

5 Assume a single uniform block of vertically aligned text.

6 Assume a single uniform block of text.

7 Treat the image as a single text line.

8 Treat the image as a single word.

9 Treat the image as a single word in a circle.

10 Treat the image as a single character.

11 Sparse text. Find as much text as possible in no particular order.

12 Sparse text with OSD.

13 Raw line. Treat the image as a single text line, bypassing hacks that are Tesseract-specific.

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 download the .traindata file for the language you need from Github and place it in $TESSDATA_PREFIX directory (this should be the same as where the tessdata directory is installed) and it should be ready to use.

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