This package contains text detection and recognition algorithm. It is for general use: given any image input, output detection and recognition results.
The implementation of text detection is based on:
Boris Epshtein, Eyal Ofek, Yonatan Wexler, "Detecting text in natural scenes with stroke width transform," Computer Vision and Pattern Recognition (CVPR) 2010.
1. Given an input image, detecting the text region based on the algorithm described in the paper above.
2. After the text region detected, cropped out region will be fed into tesseract ocr engine.
3. Instead of directly output ocr result, another dictionary search step is performed. Using letter appearance correlation, the closest word in dictionary is found as the final output.
run test script
After compiling the code, to test the code:
roscd read_text cd scripts sh test.sh
Which will read in a test image, a letter correlation matrix and a dictionary, output an image named ***_detection.jpg under the same directory, showing detected region with acceptable ocr results and final output text in the right side of the image.
command line usage
The following is just the content in test.sh:
rosrun read_text run_detect ../images/test_image.jpg ../fonts/correlation.txt ../dictionary/full-dictionary
First input is any image file readable by OpenCV, either color or gray-scale image.
The second is a correlation measurement for letter appearance, generated from letter templates under fonts/. The correlation file is a plain text file contains 62*62 float numbers between (0-1], indicating correlation between [a-z,A-Z,0-9]. Script for generating your own correlation file is coming soon.
The third input is the dictionary file, plain ASCII file with one word each line. The default dictionary provided in the package has 74550 words with both capital initial, capital all, lower all cases(e.g. Literate, LITERATE, literate), thus in size of 223650, 3 times larger. You can easily generate your own dictionary with all cases using scripts/dictionary.py. (Note: if you give smaller dictionary, the results will be more accurate.)
batch run detection
A script for batching up detection for images under the same directory (recursively) is also provided.
mkdir results cd results python scripts/filelist.py <path/to/input/folder>
The script recursively checks all images for certain formats(see the script for more detail) under input directory and run detection, outputting results under current directory where you run the script. Note, the input images should have different names, otherwise the results will be overwritten.
1. Currently, we use linear search over dictionary when correcting OCR results. A hash based search scheme will be implemented.
2. Following text region detection, we will add an additional step to rotate text into horizontal aligned, for better OCR results.
3. Maybe rewrite implementation in stroke width transform using OpenCV line iterator, may be faster.