OMR systems form the basis of many important examinations all over the world. However, existing Optical Mark Recognition (OMR) systems tend to be expensive and rigid in their operation, often resulting in erroneous evaluations due to strict correction protocols.

Fig.: Some samples of the answer box images used in training. The samples are taken from the MC Answer Boxes dataset

Few shortcommings of the modern day OMR are summarized below.

• Extraction of skewed Region of Interests (ROI) due to misalignment of scanners.
• The omr systems are rigid and template specific. The algorithm that works for one type of OMR will not necessarily work for another.
• Thresholding based Algorithms
• No scope to cross out a particular box.

In this work we have proposed a light weight Mobile Net V2 based classifier model OMRNet. OMRNet classifies answer boxes into three class: Marked, Empty and crossed-out. Furthermore, being trained on a diverse dataset ( MC Answer Boxes dataset ) it can accomodate any skewness of answer boxes. The neural network architecture of the model is shown below.

Fig.: Transfer Learning on MobileNetV2 and our proposed model

Since OMRNet will find its major applications in resource constraint regions we have designed our model to be extremely lightweight. Our model as of 13th July, 2023 has achieved the state of the art accuracy on the dataset. However, inspite of high accuracy it is extremely lightweight. We have described in detail about its light weightedness and how we achieved it in our paper. We have applied a quantization model to reduce its size 7 times. The quantized model is just 3 MB .

Fig.: Comparison of the test accuracy of OMRNet and post quantized OMRNet.

We have a build a rest api to expose our model to a HTTP endpoint. We have also buld a website for demo. The code for the same can be found in WebApp Folder.

An snapshot of the web app we have developed. The site allows users to upload an answer box standard image formats (like .jpg or .png) and immediately predicts the class of the answer box a certain confidence web app. It uses our proposed model at the back end.

If you use our work please cite our paper by

@article{mondal_omrnet:_2023,
	title = {{OMRNet}: {A} lightweight deep learning model for optical mark recognition},
	issn = {1380-7501, 1573-7721},
	shorttitle = {Omrnet},
	url = {https://link.springer.com/10.1007/s11042-023-15408-8},
	doi = {10.1007/s11042-023-15408-8},
	language = {en},
	urldate = {2023-07-13},
	journal = {Multimedia Tools and Applications},
	author = {Mondal, Sayan and De, Pratyay and Malakar, Samir and Sarkar, Ram},
	month = jul,
	year = {2023},
}