(High Accuracy Text Detection and Recognition using ResNet as Feature Extraction

• Main Authors: Chi-Hsin Yang, 楊啟昕
• Other Authors: Chen-Chiung Hsieh
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/ry38j8

碩士 === 大同大學 === 資訊工程學系(所) === 107 === In passed decades, a complete procedure in Optical Character Recognition (OCR) has established. The procedure is mainly split into three parts: text detection, text recognition, and post-processing. In fact, traditional OCR system cannot handle scene text. Text detection in natural scene images is much more difficult than the recognition of text in scanned document images because of its complicating background. Moreover, Chinese character detection and recognition are difficult topic in OCR due to its numerous characters. Popular existing methods for characters segmentation are CTPN, EAST and PixelLink. However, they are not very capable dealing with the small and densely character in large image, and connected characters. To cope with these problems, we adopted above popular character segmentation networks CTPN or EAST as the main structure and proposed using ResNet as the feature extraction network due to its excellent sensitivity of tiny features among those existing methods. The experimental result shows that the feature extraction network can affect the precision of locating text significantly. In the experiment with ICDAR dataset, the effect of deeper depth and larger width of ResNet on EAST is notable. The performance of text segmentation on ICDAR2015 is 83.4% accuracy which is 7% higher than original PVANET-EAST. The text detection network also have great ability of generalization that can detect untrained scanned document in 83.9% accuracy and Chinese calligraphy in 86.3% accuracy.