Abstract :
In practical applications of document understanding, if the documents have multiple languages and orientations, the conventional OCR systems can not be directly applied. This is because those OCR systems are usually designed for texts of single language and normal orientation. To solve this problem, many non-character based recognition approaches were proposed. However, the performance of those methods were not comparable with the mature OCR systems. Consequently, a better idea is to recognize the language type and orientation before the OCR is applied. Besides, the characters of different languages have very ambiguous shape, so it is very difficult to extract stable feature for the recognition. Recently, the convolutional neural networks (CNN) have achieved great success in pattern recognition tasks. Therefore, for such difficult tasks, the CNN is one of the best choice. In this paper, we first applied CNN to the recognition of the document properties. A novel sliding window voting process is proposed to reduce the network scale and fully use the information of the text line. In the experiments, our method had very high recognition rate. The results proved the advantage of the proposed method and which also can be applied to create a document understanding system with OCR systems.
