Preliminary Transformations Presented By: -Mona Saudagar Under Guidance of: - Prof. S. V. Jain Multi Oriented Text Recognition In Digital Images

Introduction  Among all the contents in images, text information has inspired great interests, since it can be easily understood by both human and computer  Text in the image contains useful information which helps to acquire the overall idea behind the image.  Lot of existing text detection and recognition systems are considered for horizontal or near horizontal texts but detecting texts of random orientations from images has become an increasingly important and yet challenging task.

Introduction  Detecting texts of random orientations from images is a challenging problem due to the variety of fonts, sizes, styles, orientations, alignment effects, reflections, shadows, the complexity of image background.  As a result, normal document OCR does not give accurate recognition text due to the above mentioned factors.

Applications Extraction and recognition of text from various types of images, are very effectual in text based application like:  Video and image database retrieval  Image annotation  Data mining  Detection of vehicle license plate  Automatic detection of street name, location, traffic warning and name of commercial goods

Text information extraction system Text information extraction system Are there any text in image Where is the text How can separate text from background Textual Content Input Image Input Image Text Detection Text Detection Text Localizati on Text Localizati on Text Extraction Text Extraction Output Image Output Image

According to literature in text recognition, we have three classes of methods that are: (i) Methods to recognize the segmented text by proposing their own features with classifiers (ii) Methods to binarize and recognize the text without segmentation of text lines using multiple hypotheses frames works (iii) Methods to enhance the text through binarization to improve recognition rate

Hidden Markov Models (HMM) Approach consist of four phases: 1. Binarization of Text Line 2.Path of Sliding Window 3.Feature Extraction 4. Hidden Markov Models

1. Binarization of Text Line  Wavelet-Gradient-Fusion method to convert text line image into binary image.  Fuses of horizontal, vertical and diagonal information obtained by the wavelet and the gradient on text line images to enhance the text information.  An unsupervised clustering algorithm is applied on row wise and column-wise pixels separately to extract possible text information.

(a). Input text line image (b) Horizontal Wavelet (c) Horizontal Gradient (d) Fusion-1 of (b) and (c) (e) Vertical Wavelet (f) Vertical Gradient (g) Fusion-2 of (e) and (f) (h) Diagonal Wavelet (i) Diagonal Gradient (j) Fusion-3 of (h) and (i) (k) Fusion of Fusion-1, Fusion-2 and Fusion-3

2.Path of Sliding Window  For feature analysis, the fixed width sliding window is placed at the left most position of the curved line and is moved to the next positions in steps.

At each position, the sliding window is subdivided into rectangular cells.

3. Feature Extraction - Two types of features were used: Marti- Bunke feature: The sliding window has a width of 1 pixel, moving from left to right and at each position 9 geometrical features are extracted. LGH feature : Based on the calculation of the local gradient histogram. Here, a sliding window traverses the image and each window is sub-divided into 4 × 4 regular cells. From all pixels in each cell a histogram of gradient orientations is calculated. Considered 8 orientations thus the final feature vector which is the concatenation of the 16 histograms results in a feature vector containing 128features.

4. Hidden Markov Models The text recognition system is performed using HMMs. The basic models considered in this approach are character models.

Performance CHARACTER AND WORD RECOGNITION PERFORMANCE BY HMM METHOD (%) : DatasetsAccuracy (%) CharacterWord ICDAR MSRA-TD NUS

Why accuracy is not high?  Some wrong recognition results of characters obtained from the HMM method.  Analyzing the recognition result, we find classification errors and noted that those errors are mainly caused by ambiguous characters, such as {L, I}, {O, D}, {h, n}, {e, c} etc.

Conclusion  Due to the variety of orientation and complex backgrounds, text reading from natural scene images is still an unsolved problem.  Though we have large no of algorithms and methods for text extraction from image but none of them provide a adequate output because of deviation in text.

Future Scope  As we find classification errors mainly caused by ambiguous characters we can make further improvements by making features of characters more robust.  Available techniques can also be extended to work with touching and broken characters.

References  S. Roy, P. P. Roy, P. Shivakumara, G. Louloudis, C. L. Tan, HMM-based Multi Oriented Text Recognition in Natural Scene Image© 2013 IEEE  Lukas Neumann Jirı Matas, Real-Time Scene Text Localization and Recognition©2012 IEEE  S. Grover, K. Arora, S. K. Mitra, Text Extraction from Document Images using Edge Information IEEE Indian Council Conference© 2009

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