A Review on Intelligent Scene Text Recognition of Natural Images

This paper provides an algorithm for detection and reading of a particular text given in natural images. Scene text recognition has inspired a good interest for computer vision community in recent years. In this paper we proposed text recognition method integrating structure-guided character detection of natural images present in surroundings. From the dataset, we manually label and extract the text region. Then next we perform statistical analysis of the text region to determine which image features are reliable indicators of text and have low entropy.We use part-based tree structure to model each category of characters so as to detect and recognize characters simultaneously

A New Approach for Text String Detection from Natural Scenes By Grouping & Partition

In this paper we have reviewed and analyzed different methods to find strings of characters from natural scene images. We have reviewed different techniques like extraction of character string regions from scenery images based on contours and thickness of characters, efficient binarization and enhancement technique followed by a suitable connected component analysis procedure, text string detection from natural scenes by structure - based partition and grouping, and a robust algorithm for text detection in images. It is assumed that characters have closed contours, and a character string consists of characters which lie on a straight line in most cases. Therefore, by extracting closed contours and searching neighbors of them, character string regions can be extracted; Image binarization successfully processed natural scene images having shadows, non - uniform illumination, low contrast and large signal - dependent noise. Connected component analysis is used to define the final binary images that mainly consist of text regions. One technique chooses the candidate text characters from connected components by gradient feature and color feature. The text line grouping method performs Hough transform to fit text line among the centroids of text candidates. Each fitte d text line describes the orientation of a potential text string. The detected text string is presented by a rectangle region coveri ng all characters whose centroids are cascaded in its text line. To improve efficiency and accuracy, our algorithms are carried out in multi - scales. The proposed methods outperform the state - of - the - art results on the public Robust Reading Dataset, which contains text only in horizontal orientation. Furthermore, the effectiveness of our methods to detect text strings with arbitrary orientations is evaluated on the Oriented Scene Text Dataset collected by ourselves containing text strings in no horizontal orientations

EAST: An Efficient and Accurate Scene Text Detector

Previous approaches for scene text detection have already achieved promising performances across various benchmarks. However, they usually fall short when dealing with challenging scenarios, even when equipped with deep neural network models, because the overall performance is determined by the interplay of multiple stages and components in the pipelines. In this work, we propose a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes. The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images, eliminating unnecessary intermediate steps (e.g., candidate aggregation and word partitioning), with a single neural network. The simplicity of our pipeline allows concentrating efforts on designing loss functions and neural network architecture. Experiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500 demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR 2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps at 720p resolution.Comment: Accepted to CVPR 2017, fix equation (3

Rotation-invariant features for multi-oriented text detection in natural images.

Texts in natural scenes carry rich semantic information, which can be used to assist a wide range of applications, such as object recognition, image/video retrieval, mapping/navigation, and human computer interaction. However, most existing systems are designed to detect and recognize horizontal (or near-horizontal) texts. Due to the increasing popularity of mobile-computing devices and applications, detecting texts of varying orientations from natural images under less controlled conditions has become an important but challenging task. In this paper, we propose a new algorithm to detect texts of varying orientations. Our algorithm is based on a two-level classification scheme and two sets of features specially designed for capturing the intrinsic characteristics of texts. To better evaluate the proposed method and compare it with the competing algorithms, we generate a comprehensive dataset with various types of texts in diverse real-world scenes. We also propose a new evaluation protocol, which is more suitable for benchmarking algorithms for detecting texts in varying orientations. Experiments on benchmark datasets demonstrate that our system compares favorably with the state-of-the-art algorithms when handling horizontal texts and achieves significantly enhanced performance on variant texts in complex natural scenes