TSER: A Two-Stage Character Segmentation Network With Two-Stream Attention and Edge Refinement

Abstract

Segmenting characters in an image is a classic yet challenging task in computer vision. Correctly determining boundaries of adhesive characters with various scales and shapes is essential for character segmentation, especially for separating handwritten characters. Nevertheless, there is seldom work in the literature which can achieve satisfactory performance. In this article, by leveraging the ability of deep neural networks, we proposed a two-stage character segmentation network with two-stream attention and edge refinement (TSER) to tackle this problem. TSER firstly locates every character by object detection, then extracts their corresponding contours. In the process, a novel two-stream attention mechanism (TSAM) is proposed to make the network focus more on the discrepancy of character boundaries. Furthermore, a novel generating method is used to dynamically generate anchors on different feature levels to improve model's sensitivity on the shapes and scales of characters. Eventually a cascaded edge refinement network is used to obtain contour of each character. To prove the efficiency and generalization ability of our model, we compared TSER with traditional algorithms and other deep learning models on two commonly used datasets in different segmentation tasks. The comparative result indicated that TSER reached state-of-the-art performance.

Algorithms to Speed up Contour Tracing in Real Time Image Processing Systems

“…Contour tracing identifies the boundary pixels of the active region of an image [16]. The contour is then used, instead of the original image, to reduce the storage memory and computation time in image processing applications like biomedical imaging [17], [18], character recognition [19], [20], security and surveillance [21], [22], feature recognition using machine learning and deep learning [23]. Correspond-ing to the three existing families of contour tracing algorithms -pixel-following, vertex-following, run-data-basedfollowing -we developed three parallel processing algorithms for two-dimensional torus connected multiprocessor architectures [24].…”