https://github.com/githubharald/deslantimg

The deslanting algorithm sets text upright in images. Python, C++ and OpenCL implementations provided.
https://github.com/githubharald/deslantimg

c-plus-plus gpu handwriting-recognition image-processing ocr opencl opencv preprocessing python

Last synced: about 3 hours ago
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The deslanting algorithm sets text upright in images. Python, C++ and OpenCL implementations provided.

• Host: GitHub
• URL: https://github.com/githubharald/deslantimg
• Owner: githubharald
• License: mit
• Created: 2018-01-25T22:16:04.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2021-11-21T11:16:46.000Z (almost 3 years ago)
• Last Synced: 2023-11-07T18:15:18.965Z (about 1 year ago)
• Topics: c-plus-plus, gpu, handwriting-recognition, image-processing, ocr, opencl, opencv, preprocessing, python
• Language: C++
• Homepage:
• Size: 591 KB
• Stars: 135
• Watchers: 9
• Forks: 39
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# Deslanting Algorithm

* **Update 2021/2: added Python package**

* **Update 2021/1: added Python implementation**

This algorithm sets handwritten text in images upright, i.e. it removes the cursive writing style. One can use it as a

preprocessing step for handwritten text recognition. The following illustration shows input and output of the algorithm

for a given image (`data/test1.png`).

![deslanting](doc/example.png)

There are three implementations provided (Python, C++, OpenCL) with a focus on the Python implementation. For C++

and OpenCL see the folder [extras](extras).

## Installation

* Go to the root level of the repository

* Install by running `pip install .`

* Run `deslant_img` (without arguments) from the command line to process the images in the `data` directory (images

  taken from IAM and Bentham dataset)

* This opens a window showing the input image, deslanted image and score values

* The script can be configured via command line, see available options below, or by

  running `deslant_img -h`

![plot](doc/plot.png)

## Usage

Command line options of `deslant_img`:

```

usage: deslant_img [-h] [--data DATA] [--optim_algo {grid,powell}]

                   [--lower_bound LO] [--upper_bound HI]

                   [--num_steps STEPS] [--bg_color BG]

optional arguments:

  -h, --help            show this help message and exit

  --data DATA           directory containing the (.png|.jpg|.bmp) input images

  --optim_algo {grid,powell}

                        either do grid search, or apply Powell's derivative-

                        free optimizer

  --lower_bound LO      lower bound of shear values

  --upper_bound HI      upper bound of shear values

  --num_steps STEPS     if grid search is used, this argument defines the

                        number if grid points

  --bg_color BG         color to fill the gaps of the sheared image that is

                        returned

```

## API

* Import the `deslant_img` function as shown in the code snippet

* For documentation of parameters see command line parameters above or use `help(deslant_img)`

````python

from deslant_img import deslant_img

import cv2

import matplotlib.pyplot as plt

img = cv2.imread('data/test1.png', cv2.IMREAD_GRAYSCALE)

res = deslant_img(img)

plt.imshow(res.img)

plt.show()

````

## Algorithm

Vinciarelli and Luettin describe the algorithm in their [2001 paper](http://dx.doi.org/10.1016/S0167-8655(01)00042-3).

Here is a short outline of the algorithm:

![algo](doc/algo.png)