Implementation of scale space technique for word segmentation as proposed by R. Manmatha and N. Srimal. Even though the paper is from 1999, the method still achieves good results, is fast, and is easy to implement. The algorithm takes an image of a line as input and outputs the segmented words.
Go to the src/ directory and run the script python main.py.
The images from the data/ directory (taken from IAM dataset) are segmented into words and the results are saved to the out/ directory.
An anisotropic filter kernel is applied to the input image to create blobs corresponding to words. After thresholding the blob-image, connected components are extracted which correspond to words.
Most of the parameters of the function wordSegmentation deal with the shape of the filter kernel:
- img: grayscale uint8 image of the text-line to be segmented.
- kernelSize: size of filter kernel, must be an odd integer.
- sigma: standard deviation of Gaussian function used for filter kernel.
- theta: approximated width/height ratio of words, filter function is distorted by this factor.
- minArea: ignore word candidates smaller than specified area.
The function prepareImg can be used to convert the input image to grayscale and to resize it to a fixed height:
- img: input image.
- height: image will be resized to fit specified height.
The illustration below shows how the algorithm works:
- top left: input image.
- top right: filter kernel is applied.
- bottom left: blob image after thresholding.
- bottom right: bounding boxes around words in original image.
This algorithm gives good results on datasets with large inter-word-distances and small intra-word-distances like IAM. However, for historical datasets like Bentham or Ratsprotokolle results are not very good and more complex approaches should be used instead.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent