Object detection deep learning networks for Optical Character Recognition

Christopher Bourez, Aurelien Coquard

Abstract: In this article, we show how we applied a simple approach coming from deep learning networks for object detection to the task of optical character recognition in order to build image features taylored for documents. In contrast to scene text reading in natural images using networks pretrained on ImageNet, our document reading is performed with small networks inspired by MNIST digit recognition challenge, at a small computational budget and a small stride. The object detection modern frameworks allow a direct end-to-end training, with no other algorithm than the deep learning and the non-max-suppression algorithm to filter the duplicate predictions. The trained weights can be used for higher level models, such as, for example, document classification, or document segmentation.

Keywords: OCR, object detection, RCNN, Yolo

TL;DR: Yolo / RCNN neural network for object detection adapted to the task of OCR

Data: [ssd](https://paperswithcode.com/dataset/openlane-v1)