A Masked Bounding-Box Selection Based ResNet Predictor for Text Rotation Prediction

Abstract

The existing Optical Character Recognition (OCR) systems are capable of recognizing images with horizontal texts. However, when the rotation of the texts increases, it becomes harder to recognizing these texts. The performance of the OCR systems decreases. Thus predicting the rotations of the texts and correcting the images are important. Previous work mainly uses traditional Computer Vision methods like Hough Transform and Deep Learning methods like Convolutional Neural Network. However, all of these methods are prone to background noises commonly existing in general images with texts. To tackle this problem, in this work, we introduce a new masked bounding-box selection method, that incorporating the bounding box information into the system. By training a ResNet predictor to focus on the bounding box as the region of interest (ROI), the predictor learns to overlook the background noises. Evaluations on the text rotation prediction tasks show that our method improves the performance by a large margin.

Automated Document Orientation Correction Using Skew and Inversion Rectification With Hough Line Transformation and Machine Learning

“…A recent study by Yang et al [15] has proposed a novel deep learning-based approach to solve the deskewing problem. The proposed method involves converting the task into a classification problem by creating two classes: Horizontal and Vertical.…”

Automated Document Orientation Correction Using Skew and Inversion Rectification with Hough Line Transformation and Machine Learning

“…A recent study by Yang et al [15] has proposed a novel deep learning-based approach to solve the deskewing problem. The proposed method involves converting the task into a classi cation problem by creating two classes: Horizontal and Vertical.…”