ocr
Recognize text using optical character recognition
Description
[___] = ocr(___,
specifies options using one or more name-value arguments in addition to any combination of
arguments from previous syntaxes. For example, Name=Value)LayoutAnalysis="page"
treats the image as a page containing blocks of text.
Examples
Load an image with text into workspace.
businessCard = imread("businessCard.png");
ocrResults = ocr(businessCard)ocrResults =
ocrText with properties:
Text: '4 MathWorks:↵↵ ↵↵The MathWorks, Inc.↵↵-3 Apple Hill Drive↵Natick, MA 01760-2098↵USA↵↵www.mathworks.com↵↵ ↵↵'
CharacterBoundingBoxes: [107×4 double]
CharacterConfidences: [107×1 single]
Words: {16×1 cell}
WordBoundingBoxes: [16×4 double]
WordConfidences: [16×1 single]
TextLines: {8×1 cell}
TextLineBoundingBoxes: [8×4 double]
TextLineConfidences: [8×1 single]
recognizedText = ocrResults.Text;
figure
imshow(businessCard)
text(600,150,recognizedText,BackgroundColor=[1 1 1]);
Read an image into the workspace.
I = imread("handicapSign.jpg");Define one or more rectangular regions of interest in which to recognize text within input image.
roi = [370 246 363 423];
Alternatively, you can use drawrectangle to select a region using a mouse.
For example,
figure;imshow(I)
roi = round(getPosition(drawrectangle))
Recognize text within the ROI.
ocrResults = ocr(I,roi);
Insert the recognized text into the original image. Display the image with the inserted recognized text.
Iocr = insertText(I,roi(1:2),ocrResults.Text,AnchorPoint="RightTop",FontSize=16);
figure
imshow(Iocr)
Read an image containing a seven-segment display into the workspace.
I = imread("sevSegDisp.jpg");Specify the ROI that contains the seven-segment display.
roi = [506 725 1418 626];
To recognize the digits from the seven-segment display, specify the Model argument as "seven-segment".
ocrResults = ocr(I,roi,Model="seven-segment");Display the recognized digits and detection confidence.
fprintf("Recognized seven-segment digits: ""%s""\nDetection confidence: %0.4f",cell2mat(ocrResults.Words),ocrResults.WordConfidences)Recognized seven-segment digits: "5405.9" Detection confidence: 0.7948
Insert the recognized digits into the image.
Iocr = insertObjectAnnotation(I,"rectangle", ... ocrResults.WordBoundingBoxes,ocrResults.Words,LineWidth=5,FontSize=72); figure imshow(Iocr)
Read an image containing text into the workspace.
businessCard = imread("businessCard.png");
ocrResults = ocr(businessCard)ocrResults =
ocrText with properties:
Text: '4 MathWorks:↵↵ ↵↵The MathWorks, Inc.↵↵-3 Apple Hill Drive↵Natick, MA 01760-2098↵USA↵↵www.mathworks.com↵↵ ↵↵'
CharacterBoundingBoxes: [107×4 double]
CharacterConfidences: [107×1 single]
Words: {16×1 cell}
WordBoundingBoxes: [16×4 double]
WordConfidences: [16×1 single]
TextLines: {8×1 cell}
TextLineBoundingBoxes: [8×4 double]
TextLineConfidences: [8×1 single]
Iocr = insertObjectAnnotation(businessCard,"rectangle", ... ocrResults.WordBoundingBoxes, ... ocrResults.WordConfidences); figure imshow(Iocr)
Load an image containing text into the workspace.
businessCard = imread("businessCard.png"); ocrResults = ocr(businessCard); bboxes = locateText(ocrResults,"Math",IgnoreCase=true); Iocr = insertShape(businessCard,"FilledRectangle",bboxes); figure imshow(Iocr)
This example shows how to evaluate the accuracy of an OCR model that can recognize seven-segment numerals on a dataset. The evaluation dataset contain images of energy meter displays that have seven-segment numerals in them.
Download and extract dataset.
datasetURL = "https://ssd.mathworks.com/supportfiles/vision/data/7SegmentImages.zip"; datasetZip = "7SegmentImages.zip"; if ~exist(datasetZip,"file") disp("Downloading evaluation dataset (" + datasetZip + " - 96 MB) ..."); websave(datasetZip,datasetURL); end datasetFiles = unzip(datasetZip);
Load the evaluation ground truth.
ld = load("7SegmentGtruth.mat");
gTruth = ld.gTruth;Create datastores that contain images, bounding boxes and text labels from the groundTruth object using the ocrTrainingData function with the label and attribute names used during labeling.
labelName = "Text"; attributeName = "Digits"; [imds,boxds,txtds] = ocrTrainingData(gTruth,labelName,attributeName);
Combine the datastores.
cds = combine(imds,boxds,txtds);
Run OCR on the evaluation dataset.
results = ocr(cds, Model="seven-segment");Evaluate the OCR results against the ground truth.
metrics = evaluateOCR(results,cds);
Evaluating ocr results
----------------------
* Selected metrics: character error rate, word error rate.
* Processed 119 images.
* Finalizing... Done.
* Data set metrics:
CharacterErrorRate WordErrorRate
__________________ _____________
0.082195 0.19958
Display accuracy of the OCR model.
modelAccuracy = 100*(1-metrics.DataSetMetrics.CharacterErrorRate); disp("Accuracy of the OCR model= " + modelAccuracy + "%")
Accuracy of the OCR model= 91.7805%
Input Arguments
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN, where Name is
the argument name and Value is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Example: txt = ocr(I,LayoutAnalysis="page") treats the text in the
image as a page containing blocks of text.
Before R2021a, use commas to separate each name and value, and enclose
Name in quotes.
Type of layout analysis to perform for text segmentation, specified as one of these options:
| TextLayout Value | Text Treatment |
|---|---|
"auto" | If the |
"page" | Treats the text in the image as a page containing blocks of text. |
"block" | Treats the text in the image as a single block of text. |
"line" | Treats the text in the image as a single line of text. |
"word" | Treats the text in the image as a single word of text. |
"character" | Treats the text in the image as a single character. |
"none" | Do not perform layout analysis. Best used for images with a single line of text. |
You can use the LayoutAnalysis argument to determine the
layout of the text within the input image. For example, you can set
LayoutAnalysis to "page" to recognize text
from a scanned document that contains a specific format, such as a double column. This
setting preserves the reading order in the returned text.
If your input image contains a few regions of text, or the text is located in a
cluttered scene, the ocr function can return poor quality results.
If you get poor OCR results, try a different layout that better matches the text in
your image. If the text is located in a cluttered scene, try specifying an ROI around
the text in your image in addition to trying a different layout.
Model to use for recognition, specified as one of these options;
"english","japanese","seven-segment"— These specify the built-in models for detecting English text, Japanese text, or seven-segment digits, respectively.Character vector or string scalar — Use this option to specify a custom model or one of the additional language models included in the OCR Language Data Files support package.
Cell array of character vectors or string array — Use this option to specify multiple models to use for detection simultaneously.
For faster performance using the built-in models (including any
additional installed language models), you can append -fast to the
language model string. For example, "english-fast",
"japanese-fast", or
"seven-segment-fast".
You can also install additional models or add a custom model. For details, see Install OCR Language Data Files.
Specifying multiple languages enables simultaneous recognition of all the selected
languages. However, selecting more than one language can reduce the accuracy of the
ocr function and increase its processing time. To specify any of
the additional languages that are contained in the Install OCR Language Data Files package, specify them the same way as the built-in
languages. You do not need to specify the path. For example, to specify Finnish text
recognition:
txt = ocr(img,Model="finnish");
List of Support Package OCR Languages
To use your own custom model for a trainOCR output, you can
use this syntax:
ocrResults = ocr(I,Model=ocrModel);
ocrModel contains the full path of the model.For deployment targets generated by MATLAB® Coder™: The generated OCR executable and language model file folder must be colocated.
For English:
C:/path/to/eng.traineddataFor Japanese:
C:/path/to/jpn.traineddataFor Seven-segment:
C:/path/to/seven_segment.traineddataFor custom data files:
C:/path/to/customlang.traineddataC:/path/ocr_app.exe
You can copy the English, Japanese and seven-segment trained data files from this folder:
fullfile(matlabroot,"toolbox","vision","visionutilities","tessdata_best");
Character subset, specified as a character vector or string scalar. By default,
CharacterSet is set to the empty character vector
"", which specifies the function to search for all characters in
the language model specified by the Model name-value argument.
You can set this value to a smaller set of known characters to constrain the
classification process.
The ocr function selects the best match for detected
text from the CharacterSet. Using deducible knowledge about the
characters in the input image helps to improve text recognition accuracy. For example,
if you set CharacterSet to all numeric digits,
"0123456789", the function attempts to match each character to
only digits. In this case, the ocr function can incorrectly
recognize a non-digit character as a digit.
If you specify the Model as seven-segment,
the ocr function uses the CharacterSet
value "0123456789.:-".
Output Arguments
Tips
If your OCR results are not what you expect, try one or more of these options:
Increase the image size by 2– 4 times.
If the characters in the image are too close together or their edges are touching, use morphology to thin out the characters. Using morphology to thin out the characters helps create space between them.
Use binarization to check for non-uniform lighting issues. Use the
graythreshandimbinarizefunctions to binarize the image. If the characters are not visible in the results of the binarization, then the image has a potential non-uniform lighting issue. Try top-hat filtering, using theimtophatfunction, or other techniques that deal with removing non-uniform illumination.Use the
roiargument to isolate the text. You can specify theroimanually or use text detection.If your image looks like a natural scene containing words, such as a street scene, rather than a scanned document, try setting the
LayoutAnalysisargument to either"Block"or"Word".Ensure that the image contains dark text on a light background. To achieve this, you can binarize the image and invert it before passing it to the
ocrfunction.
References
[1] Smith, Ray. An Overview of the Tesseract OCR Engine. In Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), 629–33. IEEE, 2007. https://doi.org/10.1109/ICDAR.2007.4376991."
[2] Smith, R., D. Antonova, and D. Lee. Adapting the Tesseract Open Source OCR Engine for Multilingual OCR. Proceedings of the International Workshop on Multilingual OCR, (2009).
[3] R. Smith. Hybrid Page Layout Analysis via Tab-Stop Detection. Proceedings of the 10th international conference on document analysis and recognition. 2009.
Extended Capabilities
Version History
Introduced in R2014aSee Also
Apps
Functions
Objects
Topics
- Getting Started with OCR
- Segment and Read Text in Image
- Recognize Text Using Optical Character Recognition (OCR)
- Train an OCR Model to Recognize Seven-Segment Digits
- Recognize Seven-Segment Digits Using OCR
- Automate Ground Truth Labeling for OCR
- Install OCR Language Data Files
- Install Computer Vision Toolbox Add-on Support Files
