Chinese/English mixed Character Segmentation as Semantic Segmentation

Abstract

OCR character segmentation for multilingual printed documents is difficult due to the diversity of different linguistic characters. Previous approaches mainly focus on monolingual texts and are not suitable for multilinguallingual cases. In this work, we particularly tackle the Chinese/English mixed case by reframing it as a semantic segmentation problem. We take advantage of the successful architecture called fully convolutional networks (FCN) in the field of semantic segmentation. Given a wide enough receptive field, FCN can utilize the necessary context around a horizontal position to determinate whether this is a splitting point or not. As a deep neural architecture, FCN can automatically learn useful features from raw text line images. Although trained on synthesized samples with simulated random disturbance, our FCN model generalizes well to real-world samples. The experimental results show that our model significantly outperforms the previous methods. significantly outperform previous methods on Chi-

Segregation of meaningful strokes, a pre‐requisite for self co‐articulation removal in isolated dynamic gestures

“…Researchers customised a fully connected network (FCN) with dynamic weighted binary cross-entropy loss function to classify the splitting points (start and end of character) and non-splitting points (end of preceding and beginning of next character) of a gesture. The output of the FCN was then binarised, and the adjacent middle splitting points were formed as pairs (start and end of character), while the other points were removed [35]. Using the gesture phase segmentation dataset, they classified the gesturing phase (act) into segments (rest position, preparation, pre-stroke, strokes, post-stroke, retraction, and rest position) based on spatial and temporal features obtained from each frame using multi-layer perceptron [36].…”