Scene Text Recognition

OCRTesseract

class OCRTesseract : public BaseOCR

OCRTesseract class provides an interface with the tesseract-ocr API (v3.02.02) in C++. Notice that it is compiled only when tesseract-ocr is correctly installed.

Note

• (C++) An example of OCRTesseract recognition combined with scene text detection can be found at the end_to_end_recognition demo: https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/end_to_end_recognition.cpp
• (C++) Another example of OCRTesseract recognition combined with scene text detection can be found at the webcam_demo: https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/webcam_demo.cpp

OCRTesseract::create

Creates an instance of the OCRTesseract class. Initializes Tesseract.

C++: Ptr<OCRTesseract> OCRTesseract::create(const char* datapath=NULL, const char* language=NULL, const char* char_whitelist=NULL, int oem=(int)tesseract::OEM_DEFAULT, int psmode=(int)tesseract::PSM_AUTO)

Parameters:

• datapath – the name of the parent directory of tessdata ended with “/”, or NULL to use the system’s default directory.
• language – an ISO 639-3 code or NULL will default to “eng”.
• char_whitelist – specifies the list of characters used for recognition. NULL defaults to “0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ”.
• oem – tesseract-ocr offers different OCR Engine Modes (OEM), by deffault tesseract::OEM_DEFAULT is used. See the tesseract-ocr API documentation for other possible values.
• psmode – tesseract-ocr offers different Page Segmentation Modes (PSM) tesseract::PSM_AUTO (fully automatic layout analysis) is used. See the tesseract-ocr API documentation for other possible values.

OCRTesseract::run

Recognize text using the tesseract-ocr API. Takes image on input and returns recognized text in the output_text parameter. Optionally provides also the Rects for individual text elements found (e.g. words), and the list of those text elements with their confidence values.

C++: void OCRTesseract::run(Mat& image, string& output_text, vector<Rect>* component_rects=NULL, vector<string>* component_texts=NULL, vector<float>* component_confidences=NULL, int component_level=0)

Parameters:

• image – Input image CV_8UC1 or CV_8UC3
• output_text – Output text of the tesseract-ocr.
• component_rects – If provided the method will output a list of Rects for the individual text elements found (e.g. words or text lines).
• component_text – If provided the method will output a list of text strings for the recognition of individual text elements found (e.g. words or text lines).
• component_confidences – If provided the method will output a list of confidence values for the recognition of individual text elements found (e.g. words or text lines).
• component_level – OCR_LEVEL_WORD (by default), or OCR_LEVEL_TEXT_LINE.

OCRHMMDecoder

class OCRHMMDecoder : public BaseOCR

OCRHMMDecoder class provides an interface for OCR using Hidden Markov Models.

Note

• (C++) An example on using OCRHMMDecoder recognition combined with scene text detection can be found at the webcam_demo sample: https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/webcam_demo.cpp

OCRHMMDecoder::ClassifierCallback

Callback with the character classifier is made a class. This way it hides the feature extractor and the classifier itself, so developers can write their own OCR code.

class OCRHMMDecoder::ClassifierCallback

The default character classifier and feature extractor can be loaded using the utility funtion loadOCRHMMClassifierNM and KNN model provided in https://github.com/Itseez/opencv_contrib/blob/master/modules/text/samples/OCRHMM_knn_model_data.xml.gz.

OCRHMMDecoder::ClassifierCallback::eval

The character classifier must return a (ranked list of) class(es) id(‘s)

C++: void OCRHMMDecoder::ClassifierCallback::eval(InputArray image, std::vector<int>& out_class, std::vector<double>& out_confidence)

Parameters:

• image – Input image CV_8UC1 or CV_8UC3 with a single letter.
• out_class – The classifier returns the character class categorical label, or list of class labels, to which the input image corresponds.
• out_confidence – The classifier returns the probability of the input image corresponding to each classes in out_class.

OCRHMMDecoder::create

Creates an instance of the OCRHMMDecoder class. Initializes HMMDecoder.

C++: Ptr<OCRHMMDecoder> OCRHMMDecoder::create(const Ptr<OCRHMMDecoder::ClassifierCallback> classifier, const std::string& vocabulary, InputArray transition_probabilities_table, InputArray emission_probabilities_table, decoder_mode mode=OCR_DECODER_VITERBI)

Parameters:

• classifier – The character classifier with built in feature extractor.
• vocabulary – The language vocabulary (chars when ascii english text). vocabulary.size() must be equal to the number of classes of the classifier.
• transition_probabilities_table – Table with transition probabilities between character pairs. cols == rows == vocabulary.size().
• emission_probabilities_table – Table with observation emission probabilities. cols == rows == vocabulary.size().
• mode – HMM Decoding algorithm. Only OCR_DECODER_VITERBI is available for the moment (http://en.wikipedia.org/wiki/Viterbi_algorithm).

OCRHMMDecoder::run

Recognize text using HMM. Takes image on input and returns recognized text in the output_text parameter. Optionally provides also the Rects for individual text elements found (e.g. words), and the list of those text elements with their confidence values.

C++: void OCRHMMDecoder::run(Mat& image, string& output_text, vector<Rect>* component_rects=NULL, vector<string>* component_texts=NULL, vector<float>* component_confidences=NULL, int component_level=0)

Parameters:

• image – Input image CV_8UC1 with a single text line (or word).
• output_text – Output text. Most likely character sequence found by the HMM decoder.
• component_rects – If provided the method will output a list of Rects for the individual text elements found (e.g. words).
• component_text – If provided the method will output a list of text strings for the recognition of individual text elements found (e.g. words).
• component_confidences – If provided the method will output a list of confidence values for the recognition of individual text elements found (e.g. words).
• component_level – Only OCR_LEVEL_WORD is supported.

loadOCRHMMClassifierNM

Allow to implicitly load the default character classifier when creating an OCRHMMDecoder object.

C++: Ptr<OCRHMMDecoder::ClassifierCallback> loadOCRHMMClassifierNM(const std::string& filename)

Parameters:

• filename – The XML or YAML file with the classifier model (e.g. OCRHMM_knn_model_data.xml)

The default classifier is based in the scene text recognition method proposed by Lukás Neumann & Jiri Matas in [Neumann11b]. Basically, the region (contour) in the input image is normalized to a fixed size, while retaining the centroid and aspect ratio, in order to extract a feature vector based on gradient orientations along the chain-code of its perimeter. Then, the region is classified using a KNN model trained with synthetic data of rendered characters with different standard font types.

[Neumann11b]

Neumann L., Matas J.: Text Localization in Real-world Images using Efficiently Pruned Exhaustive Search, ICDAR 2011. The paper is available online at http://cmp.felk.cvut.cz/~neumalu1/icdar2011_article.pdf