CS315A Final Project Proposal May 3, 2010 - Stanford PPL

CS315A Final Project Proposal
May 3, 2010
Group Members:
Ajay Gupta (agupta74)
Scott Green (sagreen1)
Tushar Sawant (tsawant)
Tahrina Rumu (trumu)
Project Option Selection:
Programming
Description of Topic:
Optical character recognition (OCR) is the mechanical or electronic translation of
scanned images of handwritten, typewritten, or printed text, to machine encoded text.
OCR has been in development for almost 80 years, the first patent for an OCR machine
was filed by a German named Gustav Tauschek in 1929, and an American patent was
filed subsequently 1935. OCR has many applications, including use in the postal serivce,
language translation, digital libraries, and OCR is even in the hands of the general public,
in the form of mobile applications.
We are using an open source OCR software called Tesseract as a basis for parallelization.
Development on Tesseract first started in 1985 by Hewlett Packard, further development
was done by University of Northern Las Vegas, and the code was eventually released
under the Apache 2.0 license as open source. Google has used Tesseract extensively in
their google books project, which has attempted to digitize the worlds libraries.
Statement of Why:
For many years OCR has been considered a solved problem. Major work on Tesseract
was completed around 1996 and small modifications were made over the next ten years.
The emergence of commercially available multicore processors has opened up a new
field within computer science, and it is worth reconsidering old problems in order to
achieve speed gains. In the case of the Tesseract program we intend to parallelize the
recognition process in order to quickly and accurately convert multiple page documents.
Speeding the recognition process frees up computing resources, allowing more complex
comparison algorithms to be implemented. Beyond paralellization on multicore
processors, there is even more room for improvement by running OCR code on GPUs.
Our assertion is that OCR is not a solved problem.

Description of Methods to Evaluate:
Here is a brief description of the algorithm:
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
READING INPUT:
The image is read and thresholded using adaptive threshold algorithm to
create a binary image.
******************Relevant Function in source code**********************
// Copy the given image rectangle to Tesseract, with adaptive thresholding
// if the image is not already binary (in baseapi.cpp file)
void TessBaseAPI::CopyImageToTesseract(const unsigned char* imagedata,
int bytes_per_pixel,
int bytes_per_line,
int left, int top,
int width, int height)
********************************************************************
OUTLINES/BLOBS:
Connected-Component analysis is performed to extract outlines which are then nested
together to form blobs.
LINE FINDING/BASELINE FITTING:
The line finding algorithm finds the lines and associates blobs to a unique textline by
sorting and processing the blobs by x-coordinate and also keeping track of the slope (to
account for any skew). The baseline fitting algorithm (least median of squares fit) then
tries to fit a baseline to each of the lines.
*******Relevant Function in source code(in baseapi.cpp file)***********
// Find lines from the image making the BLOCK_LIST
//creates a full-page block and then runs connected component analysis and
//text line creation
void TessBaseAPI::FindLines(BLOCK_LIST* block_list)
********************************************************************
WORD/CHARACTER SEGMENTATION:
Each of the lines are then segmented into words/characters depending upon whether a
given line is fixed pitch or not. For fixed pitch spacing, each of the words are chopped off
into characters. However text lines with non-fixed (or proportional) pitch spacing, only
the words are segmented out and the chopping of the words into characters is done later
in the word recognition step.

WORD RECOGNITION:
In this step, the words of non-fixed pitch spacing are segmented into characters by
chopping joined characters and associating broken characters.
STATIC CHARACTER CLASSIFICATION:
In this step, feature extrcation is done for all the segemented characters and then
classified using a static character classifier.
********Relevant Functions in source code(in baseapi.cpp file)*********
// Recognize the tesseract global image and return the result as Tesseract
// internal structures.
PAGE_RES* TessBaseAPI::Recognize(BLOCK_LIST* block_list, ETEXT_DESC*
monitor)
// Make a text string from the internal data structures.
// The input page_res is deleted.
char* TessBaseAPI::TesseractToText(PAGE_RES* page_res)
********************************************************************
QUALITY:
Quality of words and letters is checked
WRITING OUTPUT:
Words are written out to .txt file
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Parallelization: We are planning to paralleize various functions at each step of the
algorithm. Here are some of the functions that can be parallelized:
1. Binarizing the image by applying the thresholds (calculated by adaptive threshold)
in parallel using multiple cores
2. Parallelize the connected-component labelling and line finding functions
3. Parallelize the word/character segmentation and recognitions routines by processing
lines on each of the cores in a parallel fashion using chunking or interleaved scheme
4. Parallelize the feature extraction and classification of characters by dividing up the
entire set of characters equally among all the available cores
References:
- Recognition of Handwritten Roman Script Using Tesseract OCR
http://arxiv.org/ftp/arxiv/papers/1003/1003.5891.pdf
- An Overview of the Tesseract OCR Engine
http://tesseract-ocr.repairfaq.org/downloads/tesseract_overview.pdf
- Optical Character Recognition Reference

http://www.nr.no/~eikvil/OCR.pdf
- Character Recognition Under Severe Distortion
http://www.computer.org/portal/web/csdl/doi/10.1109/ICDAR.2009.86
-- SourceCode
http://code.google.com/p/tesseract-ocr/
Resources Used / Required:
- Multicore Personal Computers
- Time on Cyclades and Niagara