LECTURE 6 TEXT PROCESSING

Transcription

1 SCIENTIFIC DATA COMPUTING 1 MTAT LECTURE 6 TEXT PROCESSING Prepared by: Amnir Hadachi Institute of Computer Science, University of Tartu amnir.hadachi@ut.ee

2 OUTLINE Aims Character Typology OCR systems Preprocessing Segmentation Feature extraction Classification

3 1. AIM

4 AIMS Text recognition is part of document analysis domain the objective behind it is: extract text and recognise it from images or sequences of characters codes.

5 2. TOPOLOGY

6 CHARACTER TYPOLOGY Machine printed text Limited vocabulary restricted vocabulary (city names, street names,...) language knowledge of contextual Handwritten text TOPOLOGY Various Alphabet Western languages (Roman, Greek, Asian (Chinese, Japanese, Korean, Thai,... Arabic alphabets Isolated characters, connected characters, cursive text

7 3. OCR

8 PRE-PROCESSING SEGMENTATION FEATURE EXTRACTION CLASSIFICATION

9 3.1 PRE-PROCESSING

10 PRE-PROCESSING Any raw data is subject to preprocessing. The preprocessing stage make sure that the data is clean and usable. The main targets of preprocessing are as follows: Binarization noise reduction stroke width normalisation Skew correction Slant removal

11 PRE-PROCESSING Binarization: binarization or thresholding refers to the conversion of a grayscale image into a binary image. We can distinguish two categories: Global: picked one threshold value and apply it for the entire document Adaptive: uses different values for each pixel according to the local area pixels.

12 PRE-PROCESSING Noise reduction: Improving the quality of the document. Two main approaches: Filtering or masks Morphological Operation (erosion, dilation, ) Normalisation: Provide a reduction in data size which can help in extracting the shape information from the data.

13 PRE-PROCESSING Skew correction: The method is used to align the paper document with the coordinate system of the scanner. this means that the skew method uses correction, projection, profiles, transformation,

14 PRE-PROCESSING Slant removal Slant of handwriting vary from a user to another. slant removal techniques are used to normalise all the characters in the document Example: Calculation of the average angle of near-vertical elements Entropy

15 3.2 SEGMENTATION

16 SEGMENTATION Text Line detection (projections, Hough transform, Word extraction (vertical projections, connected component analysis, ) Explicit segmentation Implicite segmentation

17 3.3 FEATURES EXTRACTION

18 FEATURE EXTRACTION The characters are resented by feature vectors. Identity or finger print Objective is: maximising the recognition rate with the smallest amount possible of elements. The variability and imprecision of handwriting make it very difficult tasks. Types of features: Statistical Structural Global transformations and moments

19 FEATURE EXTRACTION Statistical Features: representing characters image by statical distribution of points. The major statistical features used are: Zoning Projections and profiles Crossing and distances

20 FEATURE EXTRACTION Statistical Features: Zoning, The image are spliced into small zones. Each zones features are extracted to form the features vector. Objective: is to obtain the local characteristics instead of global characteristics.

21 FEATURE EXTRACTION Statistical Features: Zoning, density features number of foreground pixels is considered a feature.

22 FEATURE EXTRACTION Statistical Features: Zoning, direction features focuses on the contour of the character image For each zone the contour is followed and a directional histogram is obtained by analysing the adjacent pixels. Reference: Off line handwritten OCR by G. Vamvakas

23 FEATURE EXTRACTION Statistical Features: Projection histograms representing the image 2D signal into 1-D signal. These features are: Independent to noise and deformation Dependent to rotation. Projection histogram is the number of pixel in each columns and row in the image.

24 FEATURE EXTRACTION Statistical Features: Profiles Counting the number of pixels between the bounding box and the character. Can be used to: extract contour of character locate upper or lower points on the contour calculate in / out profiles of the contour.

25 FEATURE EXTRACTION Statistical Features: Crossing and distances counting the transition from background to foreground pixels along vertical and horizontal line.

26 FEATURE EXTRACTION Structural Features: Structural features can be with high tolerance to distortions and style variations. Structural features focuses on topological and geometrical properties of the character, like: branch points, strokes and their directions, inflection between points, loops, crossing point, etc.

27 FEATURE EXTRACTION Global Transformation: transformation that affect the contour of the image and can make the recognition effective such as: Fourier Transform (FT) Central Zernike

28 3.4 CLASSIFICATION

29 CLASSIFICATION Applying classification techniques such as : k-nearest Neighbour (k-nn), Bayes Classifier, Neural Networks (NN), Hidden Markov Models (HMM), Support Vector Machines (SVM), etc Reading: Pooja Kamavisdar, Sonam Saluja, Sonu Agrawal, A Survey on Image Classification Approaches and Techniques,International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 1, January 2013

30 READINGS AND REFERENCE: Teddy Mantoro, Abdul Muis Sobri, Wendi Usino, Optical Character Recognition (OCR) Performance in Server- Based Mobile Envirement, in international Conference on Advanced Computer Science Applications and Technologies, G. Vamvakas, N. Stamatopoulos,B. Gatos, I. Pratikakis and S.J. Perantonis, "Standard Database and Methods for Handwritten Greek Character Recognition", In the proc. of the 11th Panhellenic Conference on Informatics (PCI 2007),Patras,May G. Vamvakas, B. Gatos, I. Pratikakis, N. Stamatopoulos, A. Roniotis and S.J. Perantonis, "Hybrid Off-Line OCR for Isolated Handwritten Greek Characters", The Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications (SPPRA 2007), ISBN: , pp , Innsbruck, Austria, February 2007.