Table Parsing API, often referred to as OCR Table, allows applications to extract information from tables in scanned documents, images, or PDFs. The API leverages Optical Character Recognition (OCR) technology to recognize and retrieve data from tables then converts the extracted information into a structured format, such as a CSV or JSON file.
This type of technology is commonly used in business applications, where large amounts of data need to be processed and analyzed. By automating the process of extracting information from tables, the API can save time and reduce the risk of manual errors. Additionally, the structured data output by Table Parser can be easily integrated into other applications or analyzed using data analysis tools.
You can use OCR Table in numerous fields, here are some examples of common use cases:
These are just a few examples of the many different fields in which table parser APIs can be applied. The ability to extract structured data from tables and turn it into actionable information makes this API a valuable tool for a wide range of industries and applications.
While comparing Table Parser APIs, it is crucial to consider different aspects, among others, cost security and privacy. Table Parser experts at Eden AI tested, compared, and used many Table Parser APIs of the market. Here are some actors that perform well (in alphabetical order):
Amazon Web Services (AWS) provides a Table Parsing solution through its Amazon Textract service. The service uses advanced Machine Learning algorithms to analyze the structure of a document and then extract data with high accuracy, even if the table is complex or has merged cells. Amazon Textract can also handle a variety of input file formats, including PDF, JPEG, PNG, and TIFF.
Asprise offers a comprehensive set of OCR and Document Parsing tools, including Table Parser API. Asprise's solution can extract data from both PDF and image-based tables and then output it in a variety of formats including CSV and XML, making it compatible with popular spreadsheet applications, like Excel, Google Sheets, and many more. Asprise also supports multiple-language recognition for their API (en, fr, de, es, ja, ko, zh, etc.)
Google Cloud offers a suite of tools for analyzing and processing documents named Document AI. The solution can extract tables from a variety of document formats, including PDFs and scanned images, even if they are complex or have varying layouts. The API then output data in a structured format. They also offer multiple-language support, including English, French, German, Italian, Spanish, Portuguese, Dutch, Russian, and more.
Microsoft Azure offers an OCR Table API through their Form Recognizer service. By using Machine Learning algorithms, the API is trained to recognize a wide range of document layouts and can even learn to recognize new layouts over time. Azure's API can also handle tables with multiple headers and footers, making it a versatile tool for processing complex documents.
OCR.space's Table Parser can extract data from both PDF and image-based tables and output data in a variety of formats, including CSV, Excel, and JSON. The company also provides a simple REST API interface with multiple-language support, making it easy to integrate their tools into existing software projects.
Rossum uses Machine Learning algorithms to identify the layout of the table and extract the data in a structured format. It can handle tables with merged cells, multi-line cells, and other complex table structures (with or without borders, different spacing, header rows, etc.)
For all companies who use Table Parser in their software: cost and performance are real concerns. The Table Parser market is quite dense and all those providers have their benefits and weaknesses.
Performances of Table Parsing vary according to the specificity of data used by each AI engine for their model training. This means that some Table Parsing may perform great for some languages but won’t necessarily for others.
Table Parsing APIs perform differently depending on the language of the text. Some providers are specialized in specific languages. Different specificities exist in Region specialties: some Table Parsing APIs improve their machine learning algorithm to make them accurate for text in a specific language. For example, some Table Parsing APIs perform well in translating English (US, UK, Canada, South Africa, Singapore, Hong Kong, Ghana, Ireland, Australia, India, etc.), while others are specialized in Asian languages. Rare language specialty: some Table Parsing vendors care about rare languages and dialects. You can find Table Parsing APIs that allow you to process text in Gujarati, Marathi, Burmese, Pashto, Zulu, Swahili, etc.
When testing multiple Table Parsing APIs, you will find that providers' accuracy can be different according to text quality. For example, some Table Parser APIs may perform better for handwriting text while others may perform better for digital text.
Companies and developers from a wide range of industries (Social Media, Retail, Health, Finances, Law, etc.) use Eden AI’s unique API to easily integrate Table Parsing tasks in their cloud-based applications, without having to build their own solutions.
Eden AI offers multiple AI APIs on its platform amongst several technologies: Text-to-Speech, Language Detection, Sentiment Analysis, Summarization, Question Answering, Data Anonymization, Speech Recognition, and so forth.
We want our users to have access to multiple Table Parser engines and manage them in one place so they can reach high performance, optimize cost and cover all their needs. There are many reasons for using multiple APIs :
Eden AI has been made for multiple AI APIs use. Eden AI is the future of AI usage in companies. Eden AI allows you to call multiple AI APIs.
You can see Eden AI documentation here.
The Eden AI team can help you with your Table Parsing integration project. This can be done by :