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The Easiest Invoice Processing Software That Extracts Data in Seconds
Revolutionize invoice processing workflows by enabling your operations teams to extract, validate, and process invoice data 10x faster with over 99% accuracy.
Get StartedTrusted by 10,000+ data-driven businesses
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Take a Spin Around Docsumo’s Invoice Processor
Businesses Do Extraordinary Things With Docsumo
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$100 Million
Saved in processing costs
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3.4 Million
Work hours saved
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20 Million
Documents processed
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95%+
Straight-through processing achieved
2025’s Top Invoice Processing Software Ranked
Docsumo
Docsumo's invoice processing capability leverages advanced Intelligent Document Processing (IDP) technology to transform unstructured invoice data into structured, machine-readable formats. Docsumo offers a robust solution for operations and technology teams seeking to automate their finance and accounting workflows.
Key features -
- AI-powered data extraction for complex documents (invoices, bank statements, contracts).
- Excel-like data tables to view & analyze captured data.
- Multiple input methods: emails, APIs, cloud drives, local uploads.
- Customizable validation rules for accurate data and seamless integration.
- Pre-trained AI models with options for custom training on specific datasets.
- Intuitive, user-friendly interface for reduced manual efforts and errors.
- Streamlines document workflows, improves accuracy and cuts processing time.
Things to consider -
According to user reviews, no significant limitations have been reported regarding Docsumo's performance.
Pricing -
Dosumo’s pricing model is divided into Free, Growth, and Enterprise plans. The Free plan offers a free trial, which includes 100 pages per month. The price per page for the Growth plan starts at $0.3.
Amazon Textract
Amazon Textract is a machine learning (ML) service that automatically extracts text, handwriting, layout elements, and data from scanned documents. Unlike basic OCR, it can identify, interpret, and retrieve specific data from documents.
Key features -
- Automatically detects and extracts printed and handwritten text from documents.
- Identifies and extracts key-value pairs, preserving context-like fields and their values.
- Extracts table data while maintaining the structure of rows and columns.
- Recognizes layout elements like paragraphs, titles, and headers for better document understanding.
- Allows query-based extraction, retrieving specific data using natural language queries.
Things to consider -
- Accuracy for handwritten documents can be low, requiring manual intervention.
- Service can be expensive, especially for large-scale document processing.
- Limited language support is available.
Pricing -
Amazon Textract offers a pay-as-you-go pricing model, with rates varying based on the specific API used and the number of pages processed. The basic plan for 1,000 pages begins from $1.50 per page.
Google Document AI
Google Document AI extracts structured data from documents, allowing for efficient analysis, search, and storage. The Document AI suite includes pre-trained models for data extraction, the Document AI Workbench for creating custom models or enhancing existing ones, and the Document AI Warehouse for searching and storing documents.
Key features -
- Transforms scanned images and PDFs into searchable, editable text with OCR.
- Extracts key-value pairs and table data from structured forms.
- Categorizes documents using machine learning for efficient organization.
Things to consider -
- Some documentation is outdated or ambiguous, with limited code examples for various use cases.
- Instructions for training models are unclear, especially for non-technical users.
- Multilingual support is minimal.
- Data extraction from PDFs can sometimes be inaccurate, requiring manual retraining.
Pricing -
Google Document AI offers a pay-as-you-go pricing model. Basic OCR starts at $1.50 per 1,000 pages, with additional costs for more advanced features that come with the different processors.
ABBYY Flexicapture for Invoices
ABBYY Flexicapture for Invoices is an invoice data extraction application that is known for its efficiency in digitizing, editing, and managing PDFs and scanned invoices. It offers a graphical interface that allows users to scan documents, import them, and apply OCR to them.
Key features -
- Utilizes AI-powered OCR for highly accurate text recognition.
- Supports a wide range of document formats.
- Comprehensive tools for editing and managing PDFs.
- Designed to meet the needs of both businesses and individual users.
Things to consider -
- Some advanced features may have a steeper learning curve for users.
- The pricing can be higher compared to more basic OCR solutions.
Pricing -
ABBYY offers only custom subscription plans for the FlexiCapture solution, catering to businesses and individual’s requirements.
Tungsten InvoiceAgility
Tungsten InvoiceAgility is a comprehensive invoice processing platform that combines data extraction, workflow automation, and AI-driven insights to streamline business processes.
Key features -
- Advanced OCR and intelligent document processing capabilities for extracting data from various document types.
- Low-code/no-code workflow automation tools for designing and implementing complex business processes.
- AI and machine learning integration for enhanced data extraction and process optimization.
Things to consider -
- Some users report occasional technical glitches and system restarts.
- The learning curve can be steep for non-technical users, especially for advanced features.
- Pricing may be higher compared to some competitors, particularly for cloud-based versions.
- Documentation and training resources could be improved for easier adoption.
Pricing -
Tungsten InvoiceAgility offers customized pricing based on specific business needs and deployment options.
Parashift
Parashift is an AI-powered intelligent invoice processor that automates data extraction from varied invoice templates, using advanced machine learning algorithms.
Key features -
- Offers limited set of pre-trained document types.
- Provides API and webhook access to setup seamless integration with existing systems using multiple connectors and third-party applications.
Things to consider -
- Custom model training requires at least 200 document samples, which can be time-consuming.
- Limited to processing PDFs up to 20 MB in size, potentially problematic for large or complex invoices.
- Lacks some advanced features like GenAI document summarization and duplicate file detection.
- Basic dashboard functionality may not meet advanced analytics needs.
Pricing -
Parashift uses a volume-based pricing model. The entry-level price starts at $75 per month for 500 documents.
Four Ways Docsumo Automates the Invoice Processing Workflow
Ingest, classify and pre-process any invoice format
- Send invoices via emails, API, cloud drive or local machine.
- PDF, PNG, JPG, Excel, TIFF, .TXT, Emails - Bring them all into Docsumo using our powerful APIs.
Accurately capture key values & tables from unstructured invoices
- Let’s accept it - documents like invoices are unstructured, 90% of the time.
- Docsumo’s Document AI platform enables you to extract and easily review only the fields you need from complex invoices.
Reduce errors by validating data within an invoice
- Create excel-like rules/formulae to validate extracted data within your invoice, against a database.
- Categorize table line items based on descriptions to derive key metrics required for decisioning.
Post-process and directly integrate data with downstream systems
- No matter the industry - Insurance, Underwriting, Financial Services, Lending, Logistics - we’ve got APIs ready for you.
- All that you’ve got to do is integrate the data fields in your systems with our APIs. And, you’re ready to analyze the data and make intelligent automated decisions.
How Does an Invoice Processing Platform Work in Different Scenarios?
For Invoice Images
- Advanced OCR with Deep Learning: Employs state-of-the-art Convolutional Neural Networks (CNNs) like ResNet or EfficientNet for feature extraction, combined with Transformer-based models (e.g., ViT) for contextual understanding of invoice layouts.
- Image Pre-processing: Utilizes adaptive binarization techniques (e.g., Sauvola's method) for handling varying background intensities, and applies affine transformations for skew correction and perspective rectification.
- Character Segmentation and Recognition: Implements CRAFT (Character Region Awareness for Text Detection) for robust text localization, followed by attention-based sequence-to-sequence models (e.g., CRNN with attention mechanism) for accurate character recognition.
- Post-OCR Enhancement: Employs ensemble methods combining n-gram language models and neural machine translation approaches (e.g., Transformer-based seq2seq models) for context-aware error correction.
For Structured Invoices
- Layout Analysis: Utilizes hierarchical page segmentation algorithms (e.g., MRCNN - Mask R-CNN) for precise detection of invoice regions, tables, and key-value pairs.
- Intelligent Zonal Extraction: Applies dynamic field localization using graph neural networks (GNNs) to understand spatial relationships between invoice elements.
- Data Validation and Extraction: Implements a combination of deterministic rules (regex patterns) and probabilistic approaches (CRFs - Conditional Random Fields) for robust field extraction and validation.
- Field Classification: Utilizes ensemble learning techniques combining gradient boosting models (e.g., XGBoost) with deep learning classifiers (e.g., BERT-based models fine-tuned for invoice field classification) for accurate field identification.
For Unstructured Invoices
- Advanced NLP Techniques: Employs BERT-based models (e.g., RoBERTa, ALBERT) fine-tuned on invoice corpora for contextual word embeddings and semantic understanding.
- Named Entity Recognition (NER): Utilizes state-of-the-art NER models like LUKE (Language Understanding with Knowledge-based Embeddings) or Flair, specifically trained on invoice-related entities.
- Relation Extraction: Implements graph convolutional networks (GCNs) or BERT-based relation classification models to understand complex relationships between invoice entities.
- Invoice-specific Information Extraction: Uses a combination of rule-based systems and deep learning models (e.g., BERT with pointer networks) for extracting line items, totals, and tax information.
- Document Classification: Employs hierarchical attention networks (HANs) or Longformer models for handling long invoice documents and classifying them based on type, vendor, or other criteria.
Nine Must-have Features in Your Invoice Processing App
1. Pre-Processing
OCR: Extracts high-fidelity text from images and scanned invoices, across diverse languages for document text extraction.
Auto-split: Automatically splits invoices into separate sections. This is handled by implementing rule-based logic to segment the files automatically.
Auto-classify: Automatically determines document type using an ML-based classifier to extract key features from the invoice.
Auto-orientation: Uses image processing techniques to detect and correct the orientation of invoices and images to analyze the input data effectively.
Image quality check: Leverages ML and computer vision techniques to improve the quality of images through image enhancement algorithms (e.g., auto-brightness, contrast adjustment, and noise removal).
2. Document Data Extraction & Review
Extraction via LLM (Large language model): Uses state-of-the-art AI models and transformer-based architectures capable of deeply analyzing unstructured data.
Pre-trained models: Offers AI models already trained on a data set of varied invoice layouts. Uses expert models trained on generic use cases for the highest accuracy.
Training custom models: Allows users to train their customized AI models on specific invoice data extraction use cases for best accuracy.
AI assist for key values & tables: Uses AI-driven pre-trained models to extract table data on the go, with minimal setup and effort.
Email parsing: Extracts fields and tables from email attachments using NLP techniques to extract structured data from email header, body, and attachments.
Document reviewer: Enables users to review and validate the processed invoice data, with the ability to configure specific key fields.
Few-shot learning: Helps ML models adapt to new data types with minimal training examples to improve accuracy.
Gen AI document summarizer: AI-assisted chat feature for data interaction and summary using LLMs and RAG-based applications.
3. Processing Capacity & Priority
Extract 20+ pages per document: Supports large document data extraction efficiently without a drop in performance or throughput.
Priority queue on processing: Allows faster invoice processing for exclusive users, effective for high-demand and low-latency scenarios.
4. Import & Export
Export: Supports flexible data export, supporting exporting data in CSV, Excel, and JSON formats.
APIs and webhooks access: Uses REST APIs and webhooks for programmatic interaction and event-driven workflows with minimal human touchpoints.
Native integrations: Enables seamless connectivity with upstream and downstream systems, ensuring smooth ingestion and export of data across workflows.
Custom integrations: Provides connectivity with bespoke applications, offering flexibility to integrate systems that do not fall under standard native integrations. Modern platforms like Docsumo use Document AI to reduce the time needed for integration.
5. Validation
Prompt-based validation: Instruct AI in natural language to validate specific fields.
Validation With Custom Code: Allows developers to write post-processing validation logic with custom code to refine invoice data extraction results.
Master Data Look-up: Validates data by cross-referencing with master data.
Cross-document validation: Validate by cross-referencing values across multiple documents. For example, ensuring the invoice number matches the purchase order and invoice.
External Database Validation: Validates the processed data cross-referencing against external databases with two-way or three-way matching.
6. Analytics
Document processing dashboard: Provides performance overview through the post-processing reporting dashboard with details on platform usage and data extraction accuracy metrics.
Ratios & calculated fields: Generate tailored analytics for operational and financial insights using custom metrics/ratios and fields.
Auto-categorization: Automatically organizes table line items into different categories for better workflow management.
7. Workflow
Alerts and notifications: Get real-time alerts for validation errors or successful processing milestones via Slack, Gmail, or other communication tools.
Straight-through processing: Automates the data validation based on the extraction's confidence level to enable seamless downstream transmission without human intervention.
User management: Control the number of users that can use the platform & access control.
Assign Users for Review: Ability to assign team members to fast-track review workflow.
Embeddable Review Screen: Allows integration of a temporary token into users' workflow to access and review invoices without needing login credentials.
8. Support
Support channels: Exclusive access to the customer success team to ensure uninterrupted success via email or chat.
Dedicated account manager: Ability to connect and resolve queries with dedicated automation experts.
Automation expert consultation: Access to undivided attention from automation experts over 1:1 consultation calls.
Training sessions: Allows getting up to speed with extensive training sessions.
9. Security & Compliance
Authentication: Ability to set up a social authentication process for the account.
Data center locations: Includes a region closest to the user’s location for faster processing time.
Compliance: Compliance with the highest data privacy standards by being ISO, HIPAA, SOC-2, and GDPR certified.
Best Practices To Maximize the Potential of an Invoice Processing Solution
Ensure high-quality data input
- For optimal extraction accuracy, verify that invoices are in supported formats such as PDF, TIFF, or JPEG. Advanced invoice processing software often employs deep learning models like Convolutional Neural Networks (CNNs) for image preprocessing, so starting with high-quality inputs significantly improves results.
- Ensure invoices are clear and legible, as poor-quality files can lead to errors in data extraction. Consider implementing image enhancement algorithms such as adaptive thresholding or deskewing to improve document quality before processing.
Leverage advanced OCR and NLP capabilities
- Choose invoice processing software that integrates state-of-the-art Optical Character Recognition (OCR) and Natural Language Processing (NLP) technologies. Look for solutions that employ hybrid models combining traditional OCR with deep learning approaches like LSTM (Long Short-Term Memory) networks for improved text recognition.
- For unstructured invoices, ensure the software utilizes advanced NLP techniques such as BERT (Bidirectional Encoder Representations from Transformers) or its variants for contextual understanding and entity extraction.
Configure custom extraction models
- Every business has unique invoice formats and data requirements. Utilize the software's machine learning capabilities to train custom extraction models. Implement transfer learning techniques to fine-tune pre-trained models on your specific invoice types, reducing the amount of training data required.
- Configure entity recognition models to capture industry-specific fields, and use active learning approaches to continuously improve model performance with minimal human intervention.
Implement robust validation and error handling
- Integrate multi-layered validation processes within your invoice processing workflow. Implement rule-based validation for standard fields (e.g., tax calculations, date formats) and leverage machine learning models for more complex validations.
- Consider implementing anomaly detection algorithms to flag unusual invoice patterns or amounts. Develop a comprehensive error handling system that categorizes and prioritizes exceptions, allowing for efficient human review of edge cases.
Maximize Straight-Through Processing (STP)
- Leverage the software's automation capabilities to achieve high straight-through processing rates. Implement intelligent workflow routing based on invoice characteristics, vendor profiles, or confidence scores. Utilize RPA (Robotic Process Automation) in conjunction with the invoice processing software to automate downstream tasks such as payment approvals or ERP system updates.
- Monitor STP rates and continuously optimize extraction and validation rules to increase the percentage of invoices processed without human intervention.
Integrate with existing systems
- Ensure seamless integration with your existing financial ecosystem. Utilize the software's API capabilities to establish real-time data exchange with ERP systems, accounting software, and payment platforms. Implement event-driven architectures using webhooks to trigger actions in connected systems upon invoice processing milestones.
- Consider developing custom microservices to handle specific integration requirements, ensuring scalability and maintainability of the overall solution.
FAQs
What is OCR in invoice processing?
OCR (Optical Character Recognition) in invoice processing refers to the technology that automatically extracts and converts text from scanned invoices, PDFs, or image files into structured, machine-readable data. This eliminates the need for manual data entry, speeds up the invoice handling process, and reduces errors.
How does OCR invoice processing improve efficiency in handling scanned documents?
OCR invoice processing enhances efficiency by reducing the time spent on manual data entry and verification. It automatically extracts data from scanned invoices, eliminates human errors, and allows for faster validation and approval. By automating repetitive tasks, OCR speeds up the overall payment cycle and ensures timely invoice handling.
What are the potential applications of OCR technology beyond invoice processing?
Beyond invoice processing, OCR technology can be used in a wide range of applications, including digitizing paper records, extracting data from contracts, converting printed books or documents into digital formats, processing forms, and automating data entry in industries such as healthcare, legal, and finance.
How does Docsumo automate invoice processing?
Automating invoice processing involves using OCR technology to extract data from invoices, followed by integration with accounting or ERP systems to automatically populate relevant fields, route invoices for approval, and schedule payments. The process can be enhanced further with AI and machine learning for unstructured data and custom workflows.
What are the critical differences between simple OCR and ICR for invoice processing?
Simple OCR is designed to extract printed or typed text from documents, while Intelligent Character Recognition (ICR) is an advanced form of OCR that can recognize and process handwritten text. ICR is particularly useful for processing forms or invoices that include handwritten information, offering more flexibility in document recognition.
