# Unconstrained Scene Text and Video Text Recognition for Arabic Script

arXiv: https://arxiv.org/abs/1711.02396 · HTML: https://mlsanity.com/paper/1711.02396/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [Benchmarking Scene Text Recognition in Devanagari, Telugu and Malayalam](https://mlsanity.com/paper/2104.04437/) (2021)
- [UTRNet: High-Resolution Urdu Text Recognition In Printed Documents](https://mlsanity.com/paper/2306.15782/) (2023)
- [IDPL-PFOD2: A New Large-Scale Dataset for Printed Farsi Optical Character Recognition](https://mlsanity.com/paper/2312.01177/) (2023)

## Abstract (arXiv)

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 synthesising millions of Arabic text images from a large vocabulary of Arabic words and phrases. Our implementation is built on top of the model introduced here [37] 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.