  With the advent of digital optical scanners, a lot of paper-based books,
textbooks, magazines, articles, and documents are being transformed into an
electronic version that can be manipulated by a computer. For this purpose,
OCR, short for Optical Character Recognition was developed to translate scanned
graphical text into editable computer text. Unfortunately, OCR is still
imperfect as it occasionally mis-recognizes letters and falsely identifies
scanned text, leading to misspellings and linguistics errors in the OCR output
text. This paper proposes a post-processing context-based error correction
algorithm for detecting and correcting OCR non-word and real-word errors. The
proposed algorithm is based on Google's online spelling suggestion which
harnesses an internal database containing a huge collection of terms and word
sequences gathered from all over the web, convenient to suggest possible
replacements for words that have been misspelled during the OCR process.
Experiments carried out revealed a significant improvement in OCR error
correction rate. Future research can improve upon the proposed algorithm so
much so that it can be parallelized and executed over multiprocessing
platforms.
