| dc.description.abstract | Optical Character Recognition (OCR) is a big challenge of Computer Vision. The degree of challenge has become harder from the task of recognizing the English characters and numbers with specific font and some symbol to the task of detecting and recognizing the text in the wild. And in the domain of text detection and recognition, detecting and recognizing Chinese context is more complex than the English. First, the amount of Chinese character is much more than English, and the shape is much more complex, too. Different from English context, Chinese can be written from left to right, and from top to bottom, also, which makes Chinese text detection and recognition much harder. Training a model of OCR system needs a lot of data with label, both position of the character and what the character is, the more complex scene needs more data with label. We focus on simple task, we just detect and recognize the Chinese text with the scan files. Different from task of text in wild, the block of text is more structural in task that detecting text in scan files. Therefore, we can get a great result with a simple network for text detection. And we just need to separate each line from the region that we detected, and use the line as the input of text recognition. Then, combine the result of OCR and the position we detect, we can get all the text in the scan file. And maybe, with these results, it can develop more applications, file classification takes for an example. | en_US |