Proceedings Article | 18 February 2011
KEYWORDS: Image segmentation, Optical character recognition, Image processing algorithms and systems, Detection and tracking algorithms, Image processing, Image quality, Image enhancement, Imaging systems, Cameras, Binary data
Rapid growth and progress in the medical, industrial, security and technology fields means more and more consideration
for the use of camera based optical character recognition (OCR) Applying OCR to scanned documents is quite mature,
and there are many commercial and research products available on this topic. These products achieve acceptable
recognition accuracy and reasonable processing times especially with trained software, and constrained text
characteristics. Even though the application space for OCR is huge, it is quite challenging to design a single system that
is capable of performing automatic OCR for text embedded in an image irrespective of the application. Challenges for
OCR systems include; images are taken under natural real world conditions, Surface curvature, text orientation, font,
size, lighting conditions, and noise. These and many other conditions make it extremely difficult to achieve reasonable
character recognition.
Performance for conventional OCR systems drops dramatically as the degradation level of the text image quality
increases. In this paper, a new recognition method is proposed to recognize solid or dotted line degraded characters. The
degraded text string is localized and segmented using a new algorithm. The new method was implemented and tested
using a development framework system that is capable of performing OCR on camera captured images. The framework
allows parameter tuning of the image-processing algorithm based on a training set of camera-captured text images.
Novel methods were used for enhancement, text localization and the segmentation algorithm which enables building a
custom system that is capable of performing automatic OCR which can be used for different applications.
The developed framework system includes: new image enhancement, filtering, and segmentation techniques which
enabled higher recognition accuracies, faster processing time, and lower energy consumption, compared with the best
state of the art published techniques. The system successfully produced impressive OCR accuracies (90% -to- 93%)
using customized systems generated by our development framework in two industrial OCR applications: water bottle
label text recognition and concrete slab plate text recognition. The system was also trained for the Arabic language
alphabet, and demonstrated extremely high recognition accuracy (99%) for Arabic license name plate text recognition
with processing times of 10 seconds. The accuracy and run times of the system were compared to conventional and
many states of art methods, the proposed system shows excellent results.
