We describe a novel line-level script identification method. Previous work repurposed an OCR model generating per-character script codes, counted to obtain line-level script identification. This has two shortcomings. First, as a sequence-to-sequence model it is more complex than necessary for the sequence-to-label problem of line script identification. This makes it harder to train and inefficient to run. Second, the counting heuristic may be suboptimal compared to a learned model. Therefore we reframe line script identification as a sequence-to-label problem and solve it using two components, trained end-toend: Encoder and Summarizer. The encoder converts a line image into a feature sequence. The summarizer aggregates the sequence to classify the line. We test various summarizers with identical inception-style convolutional networks as encoders. Experiments on scanned books and photos containing 232 languages in 30 scripts show 16% reduction of script identification error rate compared to the baseline. This improved script identification reduces the character error rate attributable to script misidentification by 33%.

该研究提出了一种新型的线级别手写体识别方法，并将线级别手写体识别问题重新构建为序列标签问题，使用编码器和汇总器训练端到端解决该问题，并在扫描书籍和照片中测试，在30种书写系统和232种语言中，相比传统方法，提高了16%的正确率，并减少了因识别错误导致的33%的字符错误率。

多语言OCR的序列到标签脚本识别