By far OCR is the most crucial means of digitizing hard documents. But not all OCR engines are created equal, and selecting the most apt one for your specific needs requires careful evaluation. Some OCRs are not equipped to figure out the context in a document.
Some OCRs lack the intelligence to appreciate the context of a written or printed document. They simply identify the pixels with text and pattern recognition. This limitation usually serves as a source of errors when retrieving this area of concern which can lead to bad performance in your data extraction model.
That’s why picking the right OCR engine can be tricky. To avoid making illusions about OCR engines, special attention should be paid to such parameters as: accuracy, speed, scalability & compatibility. In this blog, we’ll break down these key metrics and understand them better.
Correctness: The Ultimate Ideal in OCR
To be truly helpful and useful, any OCR engine must be accurate, and that is in fact the most important criterion. It’s what separates an effective engine that extracts clean, useful data, from a document that produces an avalanche of errors. While in most cases 98-99% accuracy rate is acceptable at page level, keep in mind that even small rates of error can accumulate for big datasets resulting in the need for tedious and time consuming manual corrections. There are also various barriers to how much achievable Optical Character Recognition (OCR) is particularly when measuring its accuracy. For instance, these include font type and clarity, language, document quality etc.
- Character Error Rate: One of the common methods of measuring accuracy is computation of Character Error Rate (CER). This is a measure of the number of characters recognized incorrectly to the number present in a document. Fewer errors committed in recognized text implies higher accuracy of documents in OCR which in turn leads to reliable and useful digital documents. For more complicated scenarios like application forms that contain handwritten texts with a lot of uncontrolled and out of vocabulary contents, even a CER can go as high as 20% and still be acceptable.
- Field-Level Accuracy: Field accuracy indicates the correctness of every individual data field that has been extracted by the OCR system. Every business is enabled to concentrate and extract relevant information from specific sections of documents with great accuracy and precision. This metric is very critical in formatting of structured and semi-structured documents, for instance where there are fields called name, date, account number, etc. With high field level accuracy, the organizations are able to eliminate much data entry workloads with a lot of inputs to reduce errors and increase efficiency in business settings making document processing intelligent and faster.
- Recall and Precision: A different issue to be addressed is precision versus recall, particularly in this scenario. Precision defines how many of the recognized characters are indeed what was supposed to be while recall defines how many characters which should have been included into recognition haven’t been included. In the majority of cases, a reasonably optimal engine would perform well across both dimensions, however depending on the nature of your use case you may emphasize one of them. To illustrate, when scanning contract pages containing sensitive information, you may consider precision to be of paramount importance.
- Error Rate: The error rate looks at how many inaccuracies are recorded in the OCR output given the predefined conditions. Each misread letter or misplaced number contributes to the cost overruns and wastage of energy. If the error rate is low, it means the accuracy of work achieved is also high and therefore, your data is not only acquired rapidly, but is also dependable. As part of OCR considerations, organizations must look for ways of minimizing error rates to maximize process automations with little manual interventions for correction, hence aiding more intelligent operations in the organization.
Speed: The Productivity Booster
Speed is one factor that serves as a distinguishing feature of an OCR solution especially when working with large batches of documents.
- Processing speed: It is often quantified in a number of pages read per minute (PPM) or characters processed per second (CPS). In organizations where documentation of huge volumes is a daily necessity, lower speed engines may cause stagnation of the workflow.
- Processing Time: Processing time means the amount time the OCR system takes to perform imaging and the recognition of text within the document. This parameter is important because it provides a means of determining how workable and functional the OCR Solution is with time. Lower the processing, more work the OCR should be able to perform in a given time.
- Image preprocessing: Some engines spend more time in document cleansing before engaging in text extraction and this may make processing speed somewhat slower but affects performance positively. If your documents are semi or unstructured, there is a high possibility that you might want to use an engine with minimum details on the text before processing emphasizing blazing fast results.
Scalability: Will Your OCR Adapt To Your Needs As You Go On?
Scalability is another very import factor to be considered while evaluating an OCR engine, especially due to the variation in data processing volumes that are anticipated. Do you think your documents or images would be higher in volume than the regular and how can you think that would be possible? Its important to know that some solutions work very well for small scale projects but when the project is scaled up to hundreds & thousands of documents, then the remedy that was successfully on smaller scales is very much likely to become ineffective.
Scalability can also be understood as the capability to acquire different types of documents at the same time. For example, your OCR engine should be able to handle invoices, handwritten forms and scanned documents, simultaneously without strain or a need for changing configurations.
- Parallel processing: Preferably OCR solutions should be those that work with cloud platforms and provide parallel operation because these types break a huge volume of data into several parts thus enhancing the speed of the process overall utility.
- Cloud-based OCR: These services can also offer the option to scale up during busy periods without having to incur costs of new hardware.
Compatibility: More Than Just a Single-Format Solution
Because the document formats are often numerous and served differently, your OCR engine should likewise be detour. Thus, a flexible OCR engine is one that supports images including title pages, digital pictures, and even wider image range formats such as ‘PDF’ or ‘DjVu’ and many others. It is essential for the engine to be able to operate in such formats in order to be effective and not incur additional formatting costs.
- Language support: This is another important factor with respect to OCR. For business with overseas markets or businesses handling foreign language documents, it is good to choose an OCR engine with many languages and scripts that can handle Hindi, Mandarin, German or Arabic scripts.
- API compatibility: It is also worth verifying if APIs are available if you plan to integrate the OCR to a wider Document Management System (DMS) or Enterprise Resource Planning (ERP) module. A lot of modern OCR systems also support REST-based API interfaces, allowing them to be incorporated in organizational processes & workflows, thus supporting more than a stand-alone solution.
- User friendly: In reality, even the most sophisticated OCR engine will not be helpful if it is very difficult to use. Thus, it is necessary to seek for engines with user friendly interface or strong customer support.
- Customizability: Some engines can be trained to learn specialized fonts or characters found in your documents and allows users to enhance the recognition process. This is where customizability comes handy.
By observing these parameters, organizations will be able to measure the efficiency of the best performing Optical Character Recognition tools and enhance them where necessary to maintain efficient, accurate and reliable document processing systems or workflows.
Conclusion
OCR engine isn’t a one-size-fits-all solution for businesses. A good way is to try multiple engines with various custom documents to check how they perform in real-world conditions.
The apt OCR engine for your specific needs will depend on the use case—whether it’s scanning of invoices at speed, preserving the accuracy of legal documents, or scaling up to process thousands of pages. By taking into consideration key metrics like accuracy, speed, scalability, and compatibility, you can assess OCR engines effectively and select the most desirable one that meets your current needs and can also scale with your business.