Unconstrained Scene Text and Video Text Recognition for Arabic Script


Building robust recognizers for Arabic has always been challenging. We demonstrate the effectiveness of an end-to-end trainable CNN-RNN hybrid architecture in recognizing Arabic text in videos and natural scenes. We outperform previous state-of-the-art on two publicly available video text datasets - ALIF and AcTiV. For the scene text recognition task, we introduce a new Arabic scene text dataset and establish baseline results. For scripts like Arabic, a major challenge in developing robust recognizers is the lack of large quantity of annotated data. We overcome this by synthesizing millions of Arabic text images from a large vocabulary of Arabic words and phrases. Our implementation is built on top of the CRNN model which is proven quite effective for English scene text recognition. The model follows a segmentation-free, sequence to sequence transcription approach. The network transcribes a sequence of convolutional features from the input image to a sequence of target labels. This does away with the need for segmenting input image into constituent characters/glyphs, which is often difficult for Arabic script. Further, the ability of RNNs to model contextual dependencies yields superior recognition results.


Fig 1: Examples of Arabic Scene Text and Video Text recognized by our model.
Our work deals only with the recognition of cropped words/lines. The bounding boxes were provided manually.

Major Contributions

  • Establish new state-of-the-art for Arabic Video Text Recognition.
  • Provide benchmark for Arabic Scene Text Recognition.
  • Release new dataset (IIIT-Arabic) for Arabic Scene Text Recognition task.

Related Publications

  • Mohit Jain, Minesh Mathew and C.V. Jawahar, Unconstrained Scene Text and Video Text Recognition for Arabic Script, Proceedings of 1st International Workshop on Arabic Script Analysis and Recognition (ASAR), Nancy, France, 2017. [PDF]


  • IIIT-Arabic Dataset
    • Curated by downloading freely available images containing Arabic script from Google Images.
    • Contains 306 full images containing Arabic and English script.
    • The full images are annotated at the word-level rendering 2198 Arabic and 2139 English word images.

Download : Dataset (108.2 MBs) || Readme


If you use this work or dataset, please cite :

  author    = "Jain, M., Mathew, M. and Jawahar, C.~V.",
  title     = "Unconstrained Scene Text and Video Text Recognition for Arabic Script",
  booktitle = "1st International Workshop on Arabic Script Analysis and Recognition, Nancy, France",
  year      = "2017",

Associated People