Cursive Scene Text Analysis by Deep Convolutional Linear Pyramids

TL;DR

This paper introduces a novel deep convolutional linear pyramid method for analyzing cursive scene text, specifically Arabic, achieving high accuracy in recognition tasks.

Contribution

It proposes a new approach using linear pyramids and empirically selected kernels for improved cursive scene text analysis, especially for Arabic script.

Findings

Achieved 0.17% error rate on Arabic scene text recognition

Utilized linear pyramids as features for scene text analysis

Demonstrated effectiveness on EASTR-42k dataset

Abstract

The camera captured images have various aspects to investigate. Generally, the emphasis of research depends on the interesting regions. Sometimes the focus could be on color segmentation, object detection or scene text analysis. The image analysis, visibility and layout analysis are the tasks easier for humans as suggested by behavioral trait of humans, but in contrast when these same tasks are supposed to perform by machines then it seems to be challenging. The learning machines always learn from the properties associated to provided samples. The numerous approaches are designed in recent years for scene text extraction and recognition and the efforts are underway to improve the accuracy. The convolutional approach provided reasonable results on non-cursive text analysis appeared in natural images. The work presented in this manuscript exploited the strength of linear pyramids by considering each pyramid as a feature of the provided sample. Each pyramid image process through various empirically selected kernels. The performance was investigated by considering Arabic text on each image pyramid of EASTR-42k dataset. The error rate of 0.17% was reported on Arabic scene text recognition.