In the realm of engaging films featuring advanced robotics, one of the enthralling aspects hinges on the incredible interactions between humans and machines. Pondering the potential changes to our world should machines commence understanding our languages both titillates and fascinates. Although this notion may be a remote concept in contemporary terms, precursors of its arrival have been embodied in the evolution of OCR technology. Since its initial conception, OCR technology’s accuracy and applicability have been steadily on the rise as experts uncover innovative uses for this tool, extending beyond the realms of image to text conversions to even security and marketing.
Demystifying OCR
OCR, also referred to as Optical Character Recognition, is an online entity utilizing sophisticated algorithms with OCR to extract text from images, hence its alternate title: the image to text tool. As its name suggests, this technological marvel appreciates language characters, assisting with their manipulation across various platforms. But before diving into the functionalities of OCR, it’s necessary to understand its roots.
The Journey of OCR
The evolution of the image to text converter tool, or OCR, is a rich tapestry of breakthroughs and milestones dating back to the early eighteen hundreds. However, the literacy skills of optical character recognition mirrors that of a growing child. Just as children gradually develop their language skills to understand written words, even in their native tongue, OCR has been steadily progressing its capabilities.
In its infancy, OCR’s reading proficiency was quite basic, though it has been continually enhancing its grasp of handwritten styles. The story of OCR’s development closely entwines with the history of scanning technology. Full-page text scanning became significantly revamped with the advent of flat-bed and drum scanners.
Moreover, technological strides have been occurring since the 1800s, with the invention of the Optophone – a groundbreaking development by the Kurzweil company, aiding those with vision impairments. This innovative period bore the creation of two subsequent groundbreaking products: the CCD Flatbed scanner and the text-to-speech synthesizer. The commercial sale of Optical Character Recognition technology happened in 1978 at a National Federation of the Blind conference. From that point onwards, OCR’s evolution soared, incorporating artificial intelligence elements into the mix.
The OCR Mechanism for Extracting Image-based Text
The process by which an OCR recognizes writing within an image involves three primary stages: preprocessing, segmentation, and recognition.
In the preprocessing phase, select portions of the text are singled out for segmentation and subsequent identification, with some characteristics possibly being discarded depending on the specific requirements of the image and recognition process. Several techniques, such as directional histograms or Hough transformation, aid in adjusting the image and determining the average text slope. Noise reduction techniques are complemented by thresholding and thinning exercises, effectively altering pixel colours and trimming character pixels respectively to achieve a text skeleton. Where necessary, thickening tactics can also be applied.
During the segmentation phase, the preprocessed text becomes segmented into characters, words, and sentences. An array of cutting practices distinguishes words within sentences, while best cuts define spaces between words. Lexical analysis methodologies are applied to find the most likely word replacements in instances where a word becomes smeared or omitted. Syntactic analysis is performed using a word recognizer to finalize the segmentation phase.
The final stage, recognition, engulfs paramount importance within the OCR process as it signifies the transformation of the extracted text into a digital format. A gamut of methods and techniques come into play for recognizing text characters. These can range from soft computing approaches and character recognition using MLP, to fuzzy genetic algorithms and generic neural networks.
Overcoming the Shortcomings of Traditional OCR
Despite its accolades, average OCR struggles with entirely negating noise and unexpected elements within the text. Its inflexibility shows when faced with different data types. For instance, some tools may struggle with recognizing tables or borders.
AI’s Impact on OCR Accuracy
Artificial intelligence (AI) is breathing fresh life into old technologies, propelling them headfirst into an era of technological evolution. Conventional OCR apparently benefits from this in several ways.
AI-enhanced OCR achieves more holistic pre-processing due to machine learning algorithms that facilitate deep learning from a wealth of data. Moreover, the utilization of intelligent data processing (IDP) augments OCR’s versatility by facilitating extraction from varied and unstructured data. This technology is now being used for understanding different text templates and delivers superior results as compared to traditional OCR.
In Closing
OCR presents a revolutionary solution for scanning and extracting text from any form of image. Despite some traditional OCR limitations, AI is actively addressing these issues. Relying on the capabilities of an OCR tool such as OnlineOCR.net to extract handwritten text or any sort of textual component within an image ensures efficient and automatic results.