OCR Data Collection Strategies: Enhancing AI/ML Models for Text Recognition
Gone are the times when the text and images needed to be manually extracted in order to propel machine learning. Not only did it lead to inefficient data digitization but it also limited digital accessibility and reduced scalability. Thanks to OCR or optical character recognition, data extraction is no more cumbersome now. It is much more efficient than ever, driving machine learning like never before.
OCR is a revolution in the digital world, enabling machine learning to reach the next level. OCR OR Optical Character Recognition is a technology that extracts texts from images or scanned documents and converts them into digital form, saving tremendous time and energy. It enables efficient data management, searchability, text analysis, and Natural Language Processing (NLP) tasks that allow insights, classification, and language model development.
In this blog, we’ll get into the nuances of OCR and learn how important it is in the machine-learning arena. We’ll get into the meaning of OCR, its process, application, techniques, and how GTS encompasses OCR in its machine-learning process. So, stay tuned.
The use of optical character recognition (OCR) technology in the business allows for the automated extraction of data from printed or handwritten text from scanned documents or image files and the subsequent conversion of the text into a machine-readable format for use in data processing operations like editing or searching.
When you scan something like a form or a receipt, your computer saves the scan as an image file. The words in an image file cannot be searched for, edited, or counted using a text editor. OCR, on the other hand, allows you to turn an image dataset for machine learning into a text document with its contents saved as text data.
How GTS Functions with OCR
Both hardware and software are components of an OCR system. The system’s objective is to scan the text of a physical paper and convert the characters it contains into a code that can be utilized for data processing. Consider this in the context of postal and mail sorting services — OCR is essential to their capacity to work swiftly in processing destinations and return addresses to sort mail more quickly and efficiently. The process does this in three steps:
Image preparation — The hardware (often an optical scanner) converts the document’s physical shape into an image in step one, such as an image of an envelope. This stage aims to make the machine’s rendition accurate while also removing any non-required aberrations. The resulting image is changed to black and white, and the contrast between the light and dark parts (characters and background) is examined. If necessary, the OCR data collection for AI/ML models may also classify the image into distinct components, such as tables, text, or inset images; at GTS all the data is sourced from handwritten documents, receipts, and many other methods.
Smart Character Recognition — AI examines the image’s shadows to detect characters and numerals. AI typically employs one of the approaches listed below to target one character, word, or block of text at a time:
Pattern recognition: GTS data collection Teams use different types of text, text formats, and handwriting to train the AI model. To find matches, the algorithm compares the characters on the scanned image of the envelope with the characters it has already learned for the data collection for AI/ML models.
Feature extraction: The algorithm applies rules about particular character properties to recognize new characters. One type of feature is the quantity of angled, crossing, or horizontal lines and curves in a character. For instance, an “H” has two vertical lines and a horizontal line in the middle; the machine will recognize all “H”s on the envelope using these feature identifiers. The characters are recognized by the system and then transformed into an ASCII code that can be utilized for further modification.
Retouching — AI fixes mistakes in the output file. One approach is to teach the AI a specific stock of words that will appear in the paper. Limit the AI’s output to just those phrases/formats to make sure that no interpretations deviate from the stocked data.
OCR has a wide range of uses, and any company that deals with physical documentation can gain from using it. Here are a few usage cases with emphasis:
The act of writing — The use of OCR for text processing may be among its earliest and most popular applications. To create editable and searchable versions of printed documents, users can scan them. The highest level of accuracy in the conversion of these documents is made possible with the aid of AI.
Legitimate Records-Â Important signed legal papers, such as loan documentation, can be stored in an electronic database with the help of OCR for quick access. The documents are also simple for many parties to see and distribute.
Retail-Â To identify their merchandise, retailers utilize serial numbers. Robots can scan product barcodes in stores or warehouses, apply OCR to extract the serial numbers from these barcodes, and then utilize that information to track stock.
Protection of the past-Â OCR converts old documents into PDF files that may be searched. Old newspapers, periodicals, letters, and other historical records will benefit significantly from this archiving.
Banking — An image of the front and back of a cheque you want to deposit can now be taken using a smartphone. The cheque can be automatically reviewed by AI-powered OCR technology to ensure that it is legitimate and that the amount deposited matches the cheque. Without the assistance of AI, OCR technology is not as advanced today. OCR and AI work together to convert documents more accurately, with fewer errors, and with additional analysis.
The Process of a Deep Learning OCR Model
Preparing the input image- In this OCR process, the text characters’ outlines are defined, meaningful edges are found, and simplification is performed. Any task that involves face recognition data collection will usually start with this phase.
Recognizing the text-Â It is necessary to build a bounding box around the text fragments on the image in this stage of an OCR project. The real-time and region-based detectors, the SSD approach, the Mask R-CNN, the EAST detector, and other heritage techniques are only a few employed for this step.
Recognition of the text-Â The text that was inserted into the bounding boxes must be recognized as the last OCR phase. Convolutional and recurrent neural networks, as well as attention processes, are widely utilized for this job, either individually or in combination. This stage may occasionally additionally contain the interpretation step, which is typical of more challenging OCR jobs like handwriting recognition and IDC.
Text Recognition AI/ML Models: OCR Data Collection Techniques
Samples of Diverse Text — Create a thorough library of text examples that contains a variety of fonts, sizes, styles, and languages. The models are exposed to a wide variety of text variations as a result of this diversity, making it difficult for them to generalize across various text forms like video data collection for AI/ML models.
Creation of synthetic data — Create synthetic text samples that resemble real-world situations using generative techniques. Burstiness is established by fusing artificially generated text with real-world examples, resulting in a mixture of hand-crafted and machine-made writing.
Types of Handwriting and Their Variations-Â Include several different types of handwriting, such as cursive, print, and creative versions. The dataset becomes more complex as a result of the incorporation of distinct handwriting styles, which also capture the delicacy of various writing methods.
Variability in Document Layout-Â Add variety to document layouts by using various alignments, spacing, and formatting types. The models are exposed to a variety of visual structures and textual layouts, which adds to the ambiguity.
Unstable Text that is Noisy-Â Include text samples that reflect actual conditions, such as fuzziness, blurriness, or low resolution. The models encounter difficult examples of degraded text, improving their capacity to handle real-world situations.
Annotation on Handwritten Text-To accurately transcribe handwritten material for training and evaluation, employ human annotators. By capturing the nuances of handwriting and enhancing identification accuracy, this method gives the dataset a human touch, whereas GTS has already implemented it in the human form by manual speech data collection for AI/Ml models.
AI/ML models for text recognition can be trained to handle a wide variety of text samples by incorporating various OCR data-collecting techniques. It is ensured that the models have the required methods to accurately recognize and interpret the text in a variety of real-world scenarios by combining diverse text samples, synthetic data, handwriting styles, layout variability, noisy text, multilingualism, contextual understanding, and human annotation.
Text extraction from photographs is currently more and more in demand. There are numerous extraction methods available for finding pertinent data. Therefore, to employ text extraction from an image in your business effectively, you should determine your business goals and analyze data that is available from both open-source and proprietary datasets. You should also decide if further security measures are necessary to establish a problem with the OCR mechanism’s correctness.
