Custom-OCR-YOLO

This is a Custom OCR built by combining YOLO and Tesseract, to read the specific contents of a Lab Report and convert it into an editable file. Here I have used YOLO_V3 trained on personal dataset. Then the coordinates of the detected objects are passed for cropping the deteted objects and storing them in another list. This list is passed through the Tesseract to get the desired output.

Model

• You can train a custom YOLO_V3 model using your own custom dataset.
• Make a folder named model and put the weights file inside it.

Data

• Link for the dataset https://drive.google.com/drive/folders/1uAc8xE6AS8YCb65iNMFyEfB5dzAWsgFN?usp=sharing

Dependencies

• Install Tesseract OCR Engine in the system https://github.com/UB-Mannheim/tesseract/wiki
• Install Pytesseract library pip install pytessercat
• Install OpenCV pip opencv

Workflow

Getting Started

python Custom_OCR.py --image <yourimage.jpg>

Step by Step Workflow

This section will highlight the steps I took in order to implement the Custom-OCR with YOLOv3 and potential areas to be worked on further.

This will show the step-by-step workflow on the following original image.

Detected regions

The first step of the process is taking the bounding box coordinates from YOLOv3 and simply taking the region within the bounds of the box. As this image is super small, we use cv2.resize() to blow the image up 3x its original size.

Then we convert the image to grayscale and apply a small Gaussian blur to smooth it out.

The image is then thresholded to white text with black background and has Otsu's method also applied. This white text on black background helps to find contours of image.

Then we apply a bitwise_not mask to flip the image to black text on white background which Tesseract is more accurate with.

The preprocessed images are then passed over Tesseract and the output is saved as a csv file.

Contributing

Currently it is in R&D Phase.

License

• MIT license