Recognition of text on word or line images, without the need for sub-word
segmentation has become the mainstream of research and development of text
recognition for Indian languages. Modelling unsegmented sequences using
Connectionist Temporal Classification (CTC) is the most commonly used approach
for segmentation-free OCR. In this work we present a comprehensive empirical
study of various neural network models that uses CTC for transcribing step-wise
predictions in the neural network output to a Unicode sequence. The study is
conducted for 13 Indian languages, using an internal dataset that has around
1000 pages per language. We study the choice of line vs word as the recognition
unit, and use of synthetic data to train the models. We compare our models with
popular publicly available OCR tools for end-to-end document image recognition.
Our end-to-end pipeline that employ our recognition models and existing text
segmentation tools outperform these public OCR tools for 8 out of the 13
languages. We also introduce a new public dataset called Mozhi for word and
line recognition in Indian language. The dataset contains more than 1.2 million
annotated word images (120 thousand text lines) across 13 Indian languages. Our
code, trained models and the Mozhi dataset will be made available at
this http URL

该论文研究了用 CTC 建立神经网络模型的方法，将 13 种印度语言的文字识别单位定为行或单词，使用合成数据训练模型，对比公共 OCR 工具，提出了新的公共数据集 Mozhi，实现了在印度语言中进行单词和行识别的 OCR。