Object Detection and Recognition of Swap-Bodies using Camera mounted on a Vehicle

Object detection and identification is a challenging area of computer vision and a fundamental requirement for autonomous cars. This project aims to jointly perform object detection of a swap-body and to find the type of swap-body by reading an ILU code using an efficient optical character recognition (OCR) method. Recent research activities have drastically improved deep learning techniques which proves to enhance the field of computer vision. Collecting enough images for training the model is a critical step towards achieving good results. The data for training were collected from different locations with maximum possible variations and the details are explained. In addition, data augmentation methods applied for training has proved to be effective in improving the performance of the trained model. Training the model achieved good results and the test results are also provided. The final model was tested with images and videos. Finally, this paper also draws attention to some of the major challenges faced during various stages of the project and the possible solutions applied.

Image Processing Based Scene-Text Detection and Recognition with Tesseract

Text Recognition is one of the challenging tasks of computer vision with considerable practical interest. Optical character recognition (OCR) enables different applications for automation. This project focuses on word detection and recognition in natural images. In comparison to reading text in scanned documents, the targeted problem is significantly more challenging. The use case in focus facilitates the possibility to detect the text area in natural scenes with greater accuracy because of the availability of images under constraints. This is achieved using a camera mounted on a truck capturing likewise images round-the-clock. The detected text area is then recognized using Tesseract OCR engine. Even though it benefits low computational power requirements, the model is limited to only specific use cases. This paper discusses a critical false positive case scenario occurred while testing and elaborates the strategy used to alleviate the problem. The project achieved a correct character recognition rate of more than 80\%. This paper outlines the stages of development, the major challenges and some of the interesting findings of the project.