Image Transcription

The role of AI and Deep Learning in Image Transcription

Image transcription involves two distinct tasks – text detection or recognition, and conversion. The latter part is easy. Once a system understands text within an image, it can easily create a digital file for it. The tricky part is to help computers or machines to identify and understand texts in the first place like a human would. That is why AI plays an important role in image transcription systems. Understanding how AI can be harnessed for voice commands is another intriguing area of development, with speech command datasets becoming increasingly relevant. For a deeper insight into this field, check out clickworker’s exploration on speech commands dataset.

The ability of AI to recognize texts within images is termed as Optical Character Recognition (OCR), which is a subset of computer vision. To detect and recognize texts as accurately as humans, AI systems are trained with a special type of neural network:

Convolutional Neural Network (CNN)

A CNN is an artificial neural network designed to analyze visual data such as image recognition. One of the key features of CNN is that it can recognize patterns within images.

It works by analyzing different aspects and elements of the image through multiple layers. This type of neural network is trained by providing large quantities of labeled images, which teaches it to identify different objects and patterns within those images and use the same logic to identify such elements in other newer images as well.

CNN is not just used for image transcription processes but in many areas where computer vision is applied. So to make it particularly useful for OCR, such networks need to be trained specifically to identify texts in images. That means the training data should comprise of images that include texts, along with respective transcriptions of those texts. Discover more about how audio annotation can further enhance the capabilities of AI systems in recognizing and processing complex auditory data.

The CNN will then compare each image with its transcription and learn to detect the text based on different patterns, for example, the curve, straight lines, and angles between the lines in each letter.
For greater efficiency, the training data, particularly in the context of audio data collection, plays a pivotal role in enhancing the performance of AI systems. It’s crucial for developing accurate and reliable voice recognition systems.

Recurrent Neural Networks (RNN)

RNNs are special neural networks that are designed to work with sequential data. They are not as impactful and important for image transcription as CNNs, but they can still prove to be useful due to their features. RNNs have two distinct functions, which can simply be termed “memory” and “prediction.”

This type of neural network analyses sequential data in steps. It then stores the output of the analysis of every step into its own memory and uses it to predict the next step of the sequence.

For image transcription, these features can be useful as it can help the AI system remember and predict different words, expressions, and patterns within texts. For example, if the system is processing an image of a receipt, it can predict that numerical digits will be expected after words like “total” or “amount.”

Combining Optical Character Recognition with Natural Language Processing

The efficiency of AI in image transcription can be further improved when combined with Natural Language Processing (NLP). Since NLP aims to make AI systems understand language the same way as humans, it can also facilitate the output of image transcription. Because even if the OCR system makes errors in the final output, the use of NLP can correct those errors by understanding the context of the text and determining the correct word.

For example, consider a receipt that contains the following text at the bottom: “Caution: Goods purchased once cannot be returned.” Let’s say that the word “Caution” isn’t clear in the image, and the AI system interprets it as “Ca t on.” Here, NPL can come into play, first analyzing this error and deducing that even if corrected to “Cat on,” it still wouldn’t make sense in the given context which talks about the purchase and return of goods. It could then come to the conclusion that it must be the word “Caution.”

Similar to this example, AI can use NLP to make contextual sense out of image-to-text transcriptions, thus further improving the efficiency of OCR systems.

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How Does Image Transcription Work?

In general, image transcription systems convert texts inside images into digital and editable formats in three steps:

Step 1: Image pre-processing

The pre-processing stage is where the system or software performs various changes and modifications to the image for one primary purpose: to make the text easily detectable.
There are many methods and technologies for image pre-processing. What method will be applied depends on how the software has been programmed, but here are some of the general methods involved in image pre-processing:

• Image Deskewing
  

  This is the process of adjusting the image’s alignment. Skewed images can make it difficult for the AI to detect text lines and word sequences, so it’s important to restore the alignment of images before processing.

• Image Denoising
  

  Photographs and scanned images can often contain unwanted elements such as distortions, lines, speckles, etc., which are referred to as “noise” in the context of image processing. These noises disrupt the clarity of images and can obstruct the AI from detecting texts. So image denoising is done to improve the clarity of the image, especially the textual elements.

• Image Normalization
  

  If the intensity values are inconsistent throughout the image, it can make it difficult to analyze certain areas. For example, in a scanned document, the bottom part of the image may be too bright, making it difficult to detect the text in that part. In such cases, image normalization is performed to adjust the intensity values at a suitable range, allowing the OCR system to identify and read the text accurately.

Video on OCR system

• Image Binarization
  

  Image binarization is the process of changing color images into binary images, i.e., black and white images. It makes the process of image transcription easy because, after binarization, the bright sections are usually the background of the document, while the textual elements will be dark. So the system ignored the bright areas and focused only on the darker regions, which assists in faster and easier text recognition.

• Image Enhancement
  

  While the other methods above were specific, image enhancement is a general term that can refer to various techniques that improve the quality of an image, and in context of OCR, the quality of text within the image to be specific.

  In fact, some of the above-mentioned methods, like normalization and denoising, are also a type of image enhancement. But apart from those, there are quite a few other techniques, such as deblurring, gamma correction, linear contrast adjustment, and median filtering, to name a few.
  AI-powered OCR systems may use any such techniques during the pre-processing stage if deemed necessary, and they can all be generalized as image enhancement.

Step 2: Character/Text Recognition

There are two primary methods for OCR systems to recognize texts and characters.

• Pattern Matching
  

  In this method, the OCR system has its own library consisting of default images or patterns for every letter and various characters known as glyphs. The system then compares different elements of the image with these glyphs and looks for similar or exact matches to deduce the text’s letters, words and characters.
  These glyphs are learned and remembered by the AI based on the training data and the variety of characters, fonts, and handwriting contained in them. The larger and more diverse the training dataset, the more efficient the system will be.

  In general, pattern matching is highly effective in recognizing printed texts as they are in standard font and can be matched accurately if the font and scaling is same. However, it’s not as effective for handwriting recognition because handwritten texts can vary greatly in terms of font and design, and cannot be matched or compared with standard datasets, patterns, or glyphs.

• Feature Recognition
  

  In this method, the system is trained to identify texts and characters by analyzing their features rather than comparing or matching them with standard glyphs stored in its own memory or database. These ‘features’ are mostly geometrical aspects such as lines, curves, angles, intersections, etc.

  Again, the efficiency of AI in understanding the features of every letter and character highly relies on the diversity and size of the training data set. This method is also effective in recognizing handwritten texts since it is not limited by the condition of matching the text with a set of standard patterns.

Step 3: Post Processing

Once the AI system or software has identified all text elements within the image, the final step involves extracting the text and finalizing it into an editable text document format. During this stage, the software may also be programmed to perform some error-checking and correction methods, such as the previously mentioned case of applying Natural Language Processing abilities.

Applications of Image Transcription

Image transcription has various real-world applications that are beneficial for both general users and businesses or organizations. Here are some of the most notable applications of this technology:

• Digital documentation and record keeping
  

  Many kinds of documents, such as reports, legal paperwork, contracts, daily logs, etc. are essential for businesses, so they need to keep a digital record of these documents. Manually preparing such digital records can be tedious and time-consuming. So a better solution is scanning such documents and transcribing the respective images.

• Accounting
  

  Image transcription can also be extremely useful in the accounting activities of companies. OCR systems can extract key information and data from images of receipts, bills, and invoices, such as the name of the party, amount, date, etc. All these data can be automatically recorded or further fed into dedicated accounting software. It’s an effective method of automating the accounting process and keeping a record of transactional details.

• Maintaining archives
  

  Using image transcription software, various important historical documents can be scanned and converted to text documents to create digital archives. Not just documents, but even artifacts and objects containing important texts can be transcribed with OCR systems.

• Medical records
  

  The medical industry is one sector where image transcription has one of the most significant applications. A large number of medical documents are hard copies, such as forms, prescriptions, and different kinds of medical records. Maintaining digital records of such documents is a must for hospitals and medical institutions, so OCR systems can be immensely useful in the healthcare sector.

Conclusion

Transcribing an image is the process of extracting texts from images and converting them to machine-readable formats so that they can be edited and modified with text editing software. This process of image-to-text conversion has widespread applications for businesses and organizations from various sectors.
With the help of technologies like deep learning neural networks and optical character recognition (OCR), AI-powered software and systems can efficiently and accurately transcribe texts from images.

So, image transcription helps to save the cost and time of maintaining digital records and documentation of important information contained within hard documents such as printed and handwritten texts, making the process much more efficient than manual transcription.