# Transforming Pixels to Words: The Evolution of Image to Text Tools


While the new data being created is saved digitally thanks to modern word processors, spreadsheets, and other relevant tools, how is digital preservation of old bulky paper files possible? The problem often arises while saving and sorting data created years ago when the trend of digital data processing was not so common. The answer to this question, according to most people, is imagery.

What if the need to modify or change the content featured in images arises? What could be the possible solution? Manual data entry is an ineffective, unproductive, tiresome, time-consuming, and inaccurate way for image to text conversion of valuable data. AI (artificial intelligence) is seen as a ground-breaking innovation that has led to the invention of highly advanced and useful technologies.

Advanced OCR technology is also one of them. OCR is an acronym for “Optical Character Recognition”. OCR mainly relies on machine learning to identify various characteristics and convert them to digitally editable text. AI-powered tools based on OCR technology can efficiently read characters and convert them into digitally editable text. Common users call them image-to-text tools.

It is worth mentioning that OCR technology existed way before AI actually emerged. Although it was not as effective as it is nowadays, OCR still found various applications. This article will discuss the evolution of OCR and image to text tools over time to help you learn everything about their history and transformation. Read on to learn more.

# Transformation of Image to Text Tools

It is worth mentioning that the history of image to text conversion goes all the way back to the telegraph era from the modern AI-based OCR technology. We will briefly discuss the various evolution phases of image to text tools. Various evolution phases are discussed in chronological order, starting from the oldest to the newest. Further details are given below.

# Beginning of OCR and Innovations

The inception of OCR goes way back to 1914, before the beginning of World War 1. Emanuel Goldberg, a physicist by profession, came up with a machine capable of reading printed text and converting it into telegraph code. The machine saw expansion in its use in the 1920s when organizations started microfilming financial records to ensure enhanced storage space. However, retrieving records efficiently from microfilms was challenging. Goldberg solved this problem with a device called “The Statistical Machine,” which was capable of using a photoelectric cell to recognize patterns using a movie projector.

# Analog Reading Machine

Moving fast forward to 1929, Gustav Tauschek, an Austrian inventor, improvised the photoelectric detector template invented by Goldberg to create an analog reading machine. This machine used a tiny window to scan images featuring text. This window had a small disk behind it. This disk started turning whenever an image was passed in front of the window. This disk featured tiny cutouts in the shape of alphabets and numbers. Once a match was found, the machine automatically triggered the corresponding letter's printing drum. The identified text was then generated on the piece of paper.

# The First OCR Machine

The 1950s marked an era of technological growth. The amount of data and information also saw increased growth during that time. Machines invented to read the text earlier could not fulfill the growing data processing needs. David Shepard and Harvey Cook Jr. invented GISMO to analyze text and convert it into machine language. It was considered the first step towards an automated data capture process. The American Magazine was the first user of the machine. Many other organizations followed it for commercial purposes.

# Further Advancements in OCR Tech

**The OCR technology saw further advancements over time, briefly discussed below.**

●The 1960s and 1970s saw multiple pattern recognition advancements.
●A new data capture technique named “Hough Transform” enabled [**OCR**](https://aws.amazon.com/what-is/ocr/) machines to capture geometric shapes in addition to English alphabets.
●MIT researchers developed ICR technology to help OCR machines decipher handwritten characters, effectively serving as a stepping stone to future machine learning advancements.
●MICR technology also emerged to help banks ensure faster check processing with the embedment of magnetic ink characters for automated and streamlined financial operations.
●The 1980s and 1990s saw digital advancements in the OCR domain, with a focus shifting to digital imaging OCR (optical character recognition).
●Based on ICR technology, OCR algorithms could identify handwritten and printed characters in various fonts and layouts.
●Companies also started paying attention to the software side of OCR technology in the late 1980s, transforming OCR software applications from experimental to practical.
●The 1990s marked the invention of commercial OCR software, making it available for individuals and business organizations.
●Digitization of archives, streamlined workflows, and searchable electronic documents were made possible because of editable text through commercial OCR software.
●Document-heavy industries could now leverage OCR for streamlined workflow and extracting text from printed files to ensure an enhanced collaborative information ecosystem.
●In 2005, the accuracy of OCR saw a significant increase because of Tesseract, which incorporated advanced machine learning and computer vision algorithms.
●The 2010s marked the deep learning revolution in the tech world. This revolution also impacted the OCR domain significantly.
●CNNs (convolutional neural networks) and RNNs (recurrent neural networks) deserve a special mention for a transformative leap forward in OCR technology.
●The accuracy rate of deep learning-based OCR was up to 99%. CNNs made image feature extraction, recognition of complex fonts, and diverse layouts easier.
●RNNs, on the other hand, played their part in refining contextual understanding and enhancing the text recognition ability of OCR technology in diverse languages and handwriting styles.

**Integration of Modern OCR in Everyday Life**

Significant enhancements in OCR technology and its combination with deep learning made it easily usable for everyone. Nowadays, highly accurate image to text tools are available online for free. For instance, you can get your hands on such a tool easily by accessing **imagetotexttools.com**. These image to text tools allow common users to leverage OCR technology for various purposes.
Even organizations can use OCR through commonly available image to text tools. Image to text conversion can find its applications everywhere, including in simple document processing, logistics, finance, real estate, and insurance. Moreover, companies can also consider it for enhanced interconnected workspaces through simultaneously viewable information.

**Putting it Together**

While we use modern image to text tools for various use cases, OCR-based image to text conversion was not always as simple and accessible as it is today. This article discusses the evolution of image to text tools, from the telegraph to the modern deep learning era. Hopefully, you will find it highly informative and beneficial!