Project Name: Optical Character Recogintion:

A powerful open-source Reinforcement learning model for detecting characters present in the image.

This repository holds the code and resources for OCR, a machine learning model trained to perform optical text recogonistion with exceptional accuracy. Built using tensorflow,numpy,pickle,matplotlib,os, the model excels at recogonising handwritten letters, making it ideal for applications such as search by image Eg. Google Images. The algorithm takes an image containing words as input and outputs the detected words. Optionally, the words are sorted according to reading order (top to bottom, left to right).

Installation

• Hit the following commands in your terminal:-

pip install numpy
pip install tensorflow
pip install pickle
pip install matplotlib

Usage

Clone the repository in your local machine,put the image that you want to predict in test_images folder and ensure that it should be in .png format. If you want to compile the model then start the training it should take around (~1.3hrs) to train the model as the data set is quite huge so to avoid that i also have a pretrained model saved in the repos named ocr_model_50_epoch.h5load it via -

custom_objects = {"CTCLayer": CTCLayer}

reconstructed_model = keras.models.load_model("./ocr_model_50_epoch.h5", custom_objects=custom_objects)

prediction_model = keras.models.Model(
  reconstructed_model.get_layer(name="image").input, reconstructed_model.get_layer(name="dense2").output
)

Algorithm

The illustration below shows how the algorithm works:

• top left: input image
• top right: apply filter to the image
• bottom left: threshold filtered image
• bottom right: compute bounding boxes

The filter kernel with size=25, sigma=5 and theta=3 is shown below on the left. It models the typical shape of a word, with the width larger than the height (in this case by a factor of 3). On the right the frequency response is shown (DFT of size 100x100). The filter is in fact a low-pass, with different cut-off frequencies in x and y direction.

Output sample

How to select parameters

• The algorithm is not scale-invariant
  • The default parameters give good results for a text height of 25-50 pixels
  • If working with lines, resize the image to 50 pixels height
  • If working with pages, resize the image so that the words have a height of 25-50 pixels
• The sigma parameter controls the width of the Gaussian function (standard deviation) along the x-direction. Small values might lead to multiply detection per word (over-segmentation), while large values might lead to a detection containing multiple words (under-segmentation)
• The kernel size depends on the sigma parameter and should be chosen large enough to contain as much of the non-zero kernel values as possible
• The average aspect ratio (width/height) of the words to be detected is a good initial guess for the theta parameter

The best way to find the optimal parameters is to use a dataset (e.g. IAM) and optimize the parameters w.r.t. some evaluation metric (e.g. intersection over union).