As technology progresses, people become more dependent on computers to help with both work and leisure activities. However, computers operate in a digital domain that requires discrete states to be identified in order for information to be processed. This is contrary to humans who function in a distinctly analog manner where occurrences are never completely black or white. Thus, a central distinction between digital and analog is that digital requires discrete states that are disjunct over time (e.g., distinct levels) while analog is continuous over time. As humans naturally operate in an analog fashion, computing technology has evolved to alleviate difficulties associated with interfacing humans to computers (e.g., digital computing interfaces) caused by the aforementioned temporal distinctions.
Technology first focused on attempting to input existing typewritten or typeset information into computers. Scanners or optical imagers were used, at first, to “digitize” pictures (e.g., input images into a computing system). Once images could be digitized into a computing system, it followed that printed or typeset material should also be able to be digitized. However, an image of a scanned page cannot be manipulated as text or symbols after it is brought into a computing system because it is not “recognized” by the system, i.e., the system does not understand the page. The characters and words are “pictures” and not actually editable text or symbols. To overcome this limitation for text, optical character recognition (OCR) technology was developed to utilize scanning technology to digitize text as an editable page. This technology worked reasonably well if a particular text font was utilized that allowed the OCR software to translate a scanned image into editable text.
However, if more complex elements were scanned in, such as equations or documents with enhanced structures (e.g., double columns, embedded pictures, etc.), traditional OCR techniques required extreme computational power to process these types of scans into recognizable matter, if processible at all. One reason for this increased difficulty is that, for example, equations tend to have complex patterns and orientations that do not fit easily into database oriented recognition systems such as those that utilize text recognition via utilization of font databases. It becomes extremely difficult, if not impossible, to store every possible equation element in a retrievable database. And, even if it was possible, the size of the database would greatly impact retrieval times of information utilized by the traditional recognition system. This issue applies equally well to document analysis and recognition as well as other two-dimensional recognition tasks.